DETAILED ACTION
Claims 1-20 of the instant application 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/31/2025 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 13-16, 18 and 20 objected to because of the following informalities: “the system computing” should read “the computing system”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. More specifically directed to the abstract idea grouping of: mathematical concept and/or mental process.
The independent claim(s) recite(s):
1. A computer-implemented method comprising:
receiving first data;
receiving user input data indicating a maximum number of consecutive words allowed to be copied from the first data when generating second data;
generating, using a trained model, the second data based on the first data and the maximum number of consecutive words allowed to be copied; and
storing the second data in a data storage.
11. A computing system comprising:
at least one processor; and
at least one memory comprising instructions that, when executed by the at least one processor, cause the computing system to:
[perform the limitations as in claim 1, above]
This reads on a human (e.g., mentally and/or using pen and paper):
Receiving first data (e.g., text or audio/speech);
Receiving data (e.g., from another human) setting a maximum number (N) of consecutive words allowed to be copied from the first data received when writing down or responding out loud with second data (e.g., summary or paraphrase);
Write down the second data on paper.
This judicial exception is not integrated into a practical application because for example: claim 1 recites “a computer-implemented method” and “data storage” while claim 11 recites additionally “a computing system”, “processor”, and “memory”. As an example, in ¶ [0154] of the as filed specification, it is disclosed: “The concepts disclosed herein may be applied within a number of different devices and computer systems, including, for example, general-purpose computing systems, speech processing systems, and distributed computing environments.”. Therefore, a general-purpose computer or computing device is described and mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical idea because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/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 elements of using a computer is listed as a general computing device as noted. The claim is not patent eligible.
With respect to claims 2 and 12, the claim(s) recite:
wherein the first data comprises a plurality of documents.
This reads on a human (e.g., mentally and/or using pen and paper):
Wherein the received first data comprises a plurality of documents.
No additional limitations are present.
With respect to claims 3 and 13, the claim(s) recite:
determining a first portion of the second data to be a sequence of words from the first data, wherein the first portion of the second data corresponds to the maximum number of consecutive words; and
after determining the first portion, determining a next word of the second data to be different than a word following the sequence of words in the first data.
This reads on a human (e.g., mentally and/or using pen and paper):
Determining a first portion of the second data (e.g., summary) to have maximum N consecutive words;
After determining N consecutive words have been met, determining a next word different than the word following the sequence of the originally received first data.
No additional limitations are present.
With respect to claims 4 and 14, the claim(s) recite:
determining the next word to be semantically similar to a word following the sequence of words in the first data.
This reads on a human (e.g., mentally and/or using pen and paper):
Determining the next word to be semantically similar to the word in the originally received first data.
No additional limitations are present.
With respect to claims 5 and 15, the claim(s) recite:
determining a first portion of the second data to be a sequence of words from the first data, wherein the first portion of the second data corresponds to the maximum number of consecutive words; and
after determining the first portion, determining a next portion of the second data to include a second portion of the first data.
This reads on a human (e.g., mentally and/or using pen and paper):
Determining a first portion of the second data (e.g., summary) to have maximum N consecutive words;
After determining N consecutive words have been met, determining a next word that is also included in the first data.
No additional limitations are present.
With respect to claims 6 and 16, the claim(s) recite:
applying a penalty function to words selected for the second data.
This reads on a human (e.g., mentally and/or using pen and paper):
Applying a set of steps (e.g., mathematical concept) to the generated second data (e.g., summary).
No additional limitations are present.
With respect to claims 7 and 17, the claim(s) recite:
wherein the penalty function increases as a number of consecutive words in the second data copied from the first data increases.
This reads on a human (e.g., mentally and/or using pen and paper):
Applying/defining a set of steps (e.g., mathematical concept) to the generated second data (e.g., summary) associated with the maximum N consecutive words.
No additional limitations are present.
With respect to claims 8 and 18, the claim(s) recite:
selecting a next word for the second data based on a penalty function applied to a prior word in the second data.
This reads on a human (e.g., mentally and/or using pen and paper):
Selecting next word based on the applied set of steps (e.g., mathematical concept) to prior words.
No additional limitations are present.
With respect to claims 9 and 19, the claim(s) recite:
wherein the trained model comprises an encoder and a decoder.
This reads on a human (e.g., mentally and/or using pen and paper):
Defining predetermined set of steps.
Additional limitations of “encoder” and “decoder” are present. Same analysis discussed in claims 1 and 11, applies.
With respect to claims 10 and 20, the claim(s) recite:
receiving audio data representing a spoken natural language input corresponding to an entity;
determining, from the data storage, the second data based on the second data corresponding to the entity;
determining, from the data storage, third data corresponding to the entity; and
presenting the second data or the third data.
This reads on a human (e.g., mentally and/or using pen and paper):
Receiving data (e.g., audio/speech) corresponding to an entity (e.g., category, name, etc.);
Determining from a predefined list or document, second data based on the entity (e.g., category, name, etc.)
Determining from a predefined list or document, third data based on the entity (e.g., category, name, etc.)
Write down the second or third data on paper.
No additional limitations are present.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 5, 9, 11, 13, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalage et al. "User-Based Personalized Text Summarizer." Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications—ICACTA 2020. Singapore: Springer Singapore, (2020) and further in view of Fan et al. (Fan, Angela, David Grangier, and Michael Auli. "Controllable abstractive summarization." arXiv preprint arXiv:1711.05217 (2017), https://arxiv.org/pdf/1711.05217).
As to independent claim 1, Nalage et al. teaches:
1. A computer-implemented method (see Fig. 3: System architecture) comprising:
receiving first data (see ¶ 1 of 3. Our Approach: “1. The system provides an interface to the user to upload the documents to be summarized.”);
receiving user input data indicating a maximum number of (see Figure 5 (System UI input) and ¶ 1 of 3. Our Approach: “2. The system also provides an interface to choose the type of user.”
Tables 3-6: ROUGE-N, GLEU, and custom scores/metrics for the different types of users: student, foreign language student, teacher, and author.);
generating, using a trained model, the second data based on the first data and the maximum number of (see Figure 5 (System UI input) and Figure 7 (System UI output) and ¶ 1 of 1. Introduction: “…Based on the type of user selected, our model uses different algorithms to summarize the document…”
¶ 1 of 3. Our Approach: “4. Based on the type of user, the system should select the appropriate algorithm to be applied on the input document.”
¶ 1 of. 4. Architecture: “Summarizer: The summarizer can be an abstractive or an extractive based on the user's input type. It summarizes the given document with the help of an appropriate algorithm and then evaluates the output summary by using a combination of ROUGEN and GLEU.
ROUGE-N: After extracting the final summary, the summary is evaluated and its semantic similarity with the original text is examined [3]. ROUGE stands for Recall Oriented Understudy for Gisting Evaluation. It calculates the number of units that overlap between human and system summary like n-gram, word sequence and pairs of words.
GLEU: It stands for Google bilingual evaluation understudy which is an algorithm for evaluating the quality of the summary. GLEU score correlates quite well with the BLEU metric on a corpus level but does not have its drawbacks for our per sentence reward objective
User: The user will input the text document and also select the type of user he/she is based on which our system will employ an algorithm to summarize the document and then deliver it to the user. The user shall have the following options as user type: 1. Student 2. Author 3. Teacher 4. Foreign language student.”
¶ 1 of 7. Conclusion and Future Scope: “…We have created a system that employs abstractive methods and trained the model in such a way that it has a refined understanding of the text in order to have an unbiased opinion in the summaries that it creates…”
Examiner notes: first data would be associated with the text inputed by the user in the System UI homepage under “Enter text”, the maximum number of words allowed to be copied would be associated with the “Summarization ratio” and/or the “Type of User” which uses different values for different types of users (i.e., custom metric based on ROUGE-N and GLEU), as disclosed by Nalage et al. and wherein the ROUGE-N is associated with the “number of units that overlap between human and system summary like n-gram…”, and the second data would be associated with the Advanced Text Summarization System UI output.);
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and storing the second data in a data storage (see ¶ 1 of. 4. Architecture: “…Database: Different summaries and their respective scores are stored in the database, which aids in calculating the threshold score for our custom metric.”).
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However, Nalage et al. does not explicitly teach, but Fan et al. does teach:
receiving user input data indicating a maximum number of consecutive words allowed to be copied from the first data when generating second data (see Table 8: entities (e.g., @entity1 [Linda MAcDonald] under b. Summary with Entity Control and @entity4[Harry Potter] under d. Remainder Summary:
d. Remainder Summary
Full Article: @entity4 [Harry Potter] star says he has no plans to fritter his cash away on fast cars, drink and celebrity parties. @entity3 [Daniel Radcliffe]’s earnings from the first five @entity4 [Harry Potter] films have been held in a trust fund which he has not been able to touch.
After 8 sentences: He’ll be able to gamble in a casino, buy a drink in a pub or see the horror film. @entity3 [Daniel Radcliffe]’s earnings from first five @entity4 [Harry Potter] films have been held in trust fund .
After 12 sentences :@entity3 [Daniel Radcliffe]’s earnings from first five @entity4 [Harry Potter] films have been held in trust fund .,
and ¶ 1 of 6 Conclusion: “We proposed a controllable summarization model to allow users to define high-level attributes of generated summaries, such as length, source-style, entities of interest, and summarizing only remaining portions of a document…”)
Here, the first number of consecutive words allowed to be copied when generating the summary is associated with the entity and the number of words included in it, as defined by the user (i.e., “Linda MacDonald” and “Harry Potter”, 2 consecutive words.;
generating, using a trained model, the second data based on the first data and the maximum number of consecutive words allowed to be copied (see Table 8 and ¶ 1 of 6 Conclusion citation(s) as in limitation above and further ¶ 3 of 2.5 Remainder Summarization: “(4) read and remainder: the model receives both read portion of the article and the remainder separated by a special token. It is trained to predict the remainder summary. We distinguish the read and remainder part of the article by using distinct sets of position embeddings…”).
Nalage et al. and Fan et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in information summarization/copying. 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 Nalage et al. to incorporate the teachings of Fan et al. of receiving user input data indicating a maximum number of consecutive words allowed to be copied from the first data when generating second data and generating, using a trained model, the second data based on the first data and the maximum number of consecutive words allowed to be copied which provides the benefit of generating summaries that follow the specified preferences wherein these control variables guide the learning process and improve generation (¶ 4 of 1. Introduction of Fan et al.).
As to independent claim 11, Nalage et al. teaches:
11. A computing system (see Fig. 3: System architecture) comprising:
at least one processor (see Fig. 3: System architecture (user device)); and
at least one memory comprising instructions that, when executed by the at least one processor (see Fig. 3: System architecture (user device)), cause the computing system to:
[perform the limitations as in claim 1, taught by Nalage et al. in combination with Fan et al., above]
Regarding claims 3 and 13, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 1 and 11, above.
Nalage et al. further teaches:
3. The computer-implemented method of claim 1, wherein generating the second data (see Figs. 5 and 7 (System UI input and output) and ¶ 1 of 1. Introduction, ¶ 1 of 3. Our Approach, ¶ 1 of. 4. Architecture and ¶ 1 of 7. Conclusion and Future Scope citations as in claims 1 and 11, above) comprises:
determining a first portion of the second data to be a sequence of words from the first data, wherein the first portion of the second data corresponds to the maximum number of consecutive words (see Fig. 5 (System UI input): “…corrupt politicians are filling their bank accounts with it. This is the reason why…” and Fig. 7 (System UI output): “…corrupt politicians are filling their bank accounts with it. The political leaders…”); and
after determining the first portion, determining a next word of the second data to be different than a word following the sequence of words in the first data (see Fig. 5 (System UI input): “…corrupt politicians are filling their bank accounts with it. This is the reason why…” and Fig. 7 (System UI output): “…corrupt politicians are filling their bank accounts with it. The political leaders…”).
13. The computing system of claim 11, wherein the instructions for generating the second data further comprise instructions that, when executed by the at least one processor (see Figure 3 (System Architecture) of Nalage et al.), cause the system computing to:
[perform the limitation(s) as in claim 3, above].
Regarding claims 5 and 15, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 1 and 11, above.
Nalage et al. further teaches:
5. The computer-implemented method of claim 1, wherein generating the second data (see Figs. 5 and 7 (System UI input and output) and ¶ 1 of 1. Introduction, ¶ 1 of 3. Our Approach, ¶ 1 of. 4. Architecture and ¶ 1 of 7. Conclusion and Future Scope citations as in claims 1 and 11, above) comprises:
determining a first portion of the second data to be a sequence of words from the first data, wherein the first portion of the second data corresponds to the maximum number of consecutive words (see Fig. 5 (System UI input): “…The political leaders… corrupt politicians are filling their bank accounts with it. This is the reason why…” and Fig. 7 (System UI output): “…corrupt politicians are filling their bank accounts with it. The political leaders…”); and
after determining the first portion, determining a next portion of the second data to include a second portion of the first data (see Fig. 5 (System UI input): “…The political leaders… corrupt politicians are filling their bank accounts with it. This is the reason why…” and Fig. 7 (System UI output): “…corrupt politicians are filling their bank accounts with it. The political leaders…”).
15. The computing system of claim 11, wherein the instructions for generating the second data further comprise instructions that, when executed by the at least one processor (see Figure 3 (System Architecture) of Nalage et al.), cause the system computing to:
[the limitation as in claim 5, above].
Regarding claims 9 and 19, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 1 and 11, above.
Fan et al. further teaches:
9 and 19. The computer-implemented method / computing system of claims 1 and 11, wherein the trained model comprises an encoder and a decoder (see ¶ Last ¶ of 2.1 Convolutional Sequence-to-Sequence: We show this simple approach is very effective. Specifically, we use byte-pair-encoding (BPE) for tokenization, a proven strategy that has been shown to improve the generation of proper nouns in translation (Sennrich et al., 2016b). We share the representation of the tokens in the encoder and decoder embeddings and in the last decoder layer.”
and 4. Experimental Setup: “Architecture, Training, and Generation: We implement models with the fairseq library1. For CNN-Dailymail, our model has 8 layers in the encoder and decoder, each with kernel width 3. We use 512 hidden units for each layer, embeddings of size 340, and dropout 0.2. For DUC, we have 6 layers in the encoder and decoder with 256 hidden units.”).
Nalage et al. and Fan et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in information summarization/copying. 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 Nalage et al. to incorporate the teachings of Fan et al. of wherein the trained model comprises an encoder and a decoder which provides the benefit of generating summaries that follow the specified preferences wherein these control variables guide the learning process and improve generation (¶ 4 of 1. Introduction of Fan et al.).
Claims 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalage et al. "User-Based Personalized Text Summarizer." Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications—ICACTA 2020. Singapore: Springer Singapore, (2020) and further in view of Fan et al. (Fan, Angela, David Grangier, and Michael Auli. "Controllable abstractive summarization." arXiv preprint arXiv:1711.05217 (2017), https://arxiv.org/pdf/1711.05217) as applied to claims 1 and 11, above and further in view of Halgamuge et al. ("The use and analysis of anti‐plagiarism software: Turnitin tool for formative assessment and feedback." Computer Applications in Engineering Education 25.6 (2017): 895-909.).
Regarding claims 2 and 12, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 1 and 11, above.
However, Nalage et al. in combination with Fan et al. does not explicitly teach, but Halgamuge et al. does teach:
2 and 12. The computer-implemented method / computing system of claims 1 and 11, wherein the first data comprises a plurality of documents (see Figure 1: Graphical summary: Analysis the use of anti-plagiarism software: Turnitin tool for formative assessment and feedback: ((1) Draft versus Final, (2) First versus Last, and (3) Course 1 versus Course 2 versus Course 3), last ¶ of 1. Introduction: “…The primary aim of this work is to investigate the benefit and reliability of Turnitin to protect students’ individual identity of their own work. A secondary aim is to determine other inputs to improve Turnitin program from the students point of view (Figure 1).”, and Table 8: Options row (Draft versus Final, First versus Last, Course 1 vs Course 2 vs Course 3)).
Nalage et al., Fan et al., and Halgamuge et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in information summarization/copying. 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 Nalage et al. in combination with Fan et al. to incorporate the teachings of Halgamuge et al. of wherein the first data comprises a plurality of documents which provides the benefit of improving the program from the user’s point of view (last ¶ of 1. Introduction Halgamuge et al.).
Claims 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalage et al. "User-Based Personalized Text Summarizer." Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications—ICACTA 2020. Singapore: Springer Singapore, (2020) and further in view of Fan et al. (Fan, Angela, David Grangier, and Michael Auli. "Controllable abstractive summarization." arXiv preprint arXiv:1711.05217 (2017), https://arxiv.org/pdf/1711.05217) as applied to claims 3 and 13, above and further in view of Podgorny et al. (US 11263277 B1).
Regarding claims 4 and 14, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 3 and 13, above.
However, Nalage et al. in combination with Fan et al. does not explicitly teach, but Podgorny et al. does teach:
4. The computer-implemented method of claim 3, further comprising determining the next word to be semantically similar to a word following the sequence of words in the first data (see ¶ in col. 5, lines 44-55: “…Alternative terms (220) are words that replace or are substituted for the query words (216). In one or more embodiments, the alternative terms (220) are not only synonyms of the corresponding replaced query words (216), but may also include semantically related words. A semantically related word is a word within a pre-determined distance of a query word (216) on a selected semantic graph data model (224). For example, the word “cat” may be semantically related to the word “dog” because the two words are within a pre-determined distance of each other on the selected semantic graph data model (224)…”
and ¶ in col. 11, lines 20-25: “…In step (310), the selected alternative terms from the data model are substituted for the query words to generate a revised query. The substitution is performed using search engine pre-processing logic. Substitution may be performed by overwriting an original query word with an alternative term…”).
Nalage et al., Fan et al., and Podgorny et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in information summarization/copying. 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 Nalage et al. in combination with Fan et al. to incorporate the teachings of Podgorny et al. of determining the next word to be semantically similar to a word following the sequence of words in the first data which provides the benefit of addressing the issue by providing a specific technical approach that teaches a computer how to substitute domain-specific words for the actual words used in a query (¶ in col. 3, lines 45-48 Podgorny et al.).
14. The computing system of claim 13, wherein the at least one memory further comprises instructions that, when executed by the at least one processor, cause the system computing (see Figure 3 (System Architecture) of Nalage et al.) to
[perform the limitation(s) as in claim 4, above].
Claims 6-8 and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalage et al. "User-Based Personalized Text Summarizer." Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications—ICACTA 2020. Singapore: Springer Singapore, (2020) and further in view of Fan et al. (Fan, Angela, David Grangier, and Michael Auli. "Controllable abstractive summarization." arXiv preprint arXiv:1711.05217 (2017), https://arxiv.org/pdf/1711.05217) as applied to claims 1 and 11, above and further in view of Song et al. ((2020). Controlling the Amount of Verbatim Copying in Abstractive Summarization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8902-8909. https://doi.org/10.1609/aaai.v34i05.6420).
Regarding claims 6 and 16, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 1 and 11, above.
However, Nalage et al. in combination with Fan et al. does not explicitly teach, but Song et al. does teach:
6. The computer-implemented method of claim 1, wherein generating the second data comprises applying a penalty function to words selected for the second data (see Figure 1, Our Approach ¶ 1-2, and Our Approach, Training ¶ 4 citations as in limitation above and Our Approach, Reranking ¶ 2-3: “We modify the original penalty term of (Yang, Huang, and Ma 2018) to make it favor summaries using more copying. In Eq. (12), we define r to be the copy rate, i.e., the percentage of summary tokens seen in the source text, scaled by a factor c. When the copy rate r is set to 1, the penalty is dropped to 0. Yang, Huang, and Ma (2018) provides a nice proof showing that this penalty term can directly translate to a coefficient multiplied to the log-likelihood score (Eq. (13)).”; and Equations 11-12: bp = min(e1−1/r, 1) [i.e., copy rate, r, associated with presence of word in source document] (11) and exp( ˆ Sbp(x, y)) = bp ·[
∏
|y|, j=1 (P(yj |y<j, x))^1/|y| [i.e., associated with r in bp equation (11), associated with presence of word in source document and the probability decreasing if for example, r=1 (i.e., more copying / consecutive words).] (13)””).
Nalage et al., Fan et al., and Song et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in information summarization/copying. 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 Nalage et al. in combination with Fan et al. to incorporate the teachings of Song et al. of wherein generating the second data comprises applying a penalty function to words selected for the second data which provides the benefit of improving the robustness and accuracy (¶ 1 of Our Approach section of Song et al.).
16. The computing system of claim 11, wherein the instructions for generating the second data further comprise instructions that, when executed by the at least one processor (see Figure 3 (System Architecture) of Nalage et al.), cause the system computing to:
[perform the limitation(s) as in claim 6, above].
Regarding claims 7 and 17, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 1 and 11, above.
However, Nalage et al. in combination with Fan et al. does not explicitly teach, but Song et al. does teach:
7 and 17. The computer-implemented method / computing system of claims 6 and 16, wherein the penalty function increases as a number of consecutive words in the second data copied from the first data increases (see Our Approach, Reranking ¶ 2-3: “BP-norm introduces a brevity penalty to summaries that do not to meet length expectation. As illustrated in Eq. (11), BP-norm performs length normalization, then adds a penalty term log bp to the scoring function. We modify the original penalty term of (Yang, Huang, and Ma 2018) to make it favor summaries using more copying. In Eq. (12), we define r to be the copy rate, i.e., the percentage of summary tokens seen in the source text, scaled by a factor c. When the copy rate r is set to 1, the penalty is dropped to 0.”).
Nalage et al., Fan et al., and Song et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in information summarization/copying. 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 Nalage et al. in combination with Fan et al. to incorporate the teachings of Song et al. of wherein generating the second data comprises applying a penalty function to words selected for the second data which provides the benefit of improving the robustness and accuracy (¶ 1 of Our Approach section of Song et al.).
Regarding claims 8 and 18, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 1 and 11, above.
However, Nalage et al. in combination with Fan et al. does not explicitly teach, but Song et al. does teach:
8. The computer-implemented method of claim 1, wherein generating the second data comprises selecting a next word for the second data based on a penalty function applied to a prior word in the second data (see Our Approach, Reranking ¶ 2-3: “BP-norm introduces a brevity penalty to summaries that do not to meet length expectation. As illustrated in Eq. (11), BP-norm performs length normalization, then adds a penalty term log bp to the scoring function. We modify the original penalty term of (Yang, Huang, and Ma 2018) to make it favor summaries using more copying. In Eq. (12), we define r to be the copy rate, i.e., the percentage of summary tokens seen in the source text, scaled by a factor c. When the copy rate r is set to 1, the penalty is dropped to 0.”).
Nalage et al., Fan et al., and Song et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in information summarization/copying. 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 Nalage et al. in combination with Fan et al. to incorporate the teachings of Song et al. of wherein generating the second data comprises selecting a next word for the second data based on a penalty function applied to a prior word in the second data which provides the benefit of improving the robustness and accuracy (¶ 1 of Our Approach section of Song et al.).
18. The computing system of claim 11, wherein the instructions for generating the second data further comprise instructions that, when executed by the at least one processor (see Figure 3 (System Architecture) of Nalage et al.), cause the system computing to
[perform the limitation(s) as in claim 8, above].
Claims 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nalage et al. "User-Based Personalized Text Summarizer." Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications—ICACTA 2020. Singapore: Springer Singapore, (2020) and further in view of Fan et al. (Fan, Angela, David Grangier, and Michael Auli. "Controllable abstractive summarization." arXiv preprint arXiv:1711.05217 (2017), https://arxiv.org/pdf/1711.05217) as applied to claims 1 and 11, above and further in view of Weng et al. (US 20160313868 A1).
Regarding claims 10 and 20, Nalage et al. in combination with Fan et al. teaches the limitations as in claims 1 and 11, above.
However, Nalage et al. in combination with Fan et al. does not explicitly teach, but Weng et al. does teach:
10. The computer-implemented method of claim 1, further comprising:
receiving audio data representing a spoken natural language input corresponding to an entity (see ¶ [0013]: “FIG. 2 is a block diagram of a multi-modal user interaction system that accepts a user's gesture and speech as inputs and that includes a multi-modal synchronization and disambiguation system, according to an embodiment.”);
determining, from the data storage, the second data based on the second data corresponding to the entity (see ¶ [0003, 0009, and 0081-0082]: “[0003] This disclosure relates generally to the field of automated information retrieval and, more specifically, to systems and methods for the retrieval and summarization of text data based on the context of a user who requests the text data.”; “[0009] … The system is also configured to collect user requests in a natural (multi-modal) and dynamic way and identify the user intent based on domain knowledge and user model to update the content or mode of presentation for the information in an interactive manner.”; “[0081] The summarization module 1024 receives clusters of news content from the news filtering module 1020 and generates multiple summaries of each story cluster that include different levels of detail... The summarization module 1024 stores the multiple levels of summarized information that are associated with each news item in the news content database 1012. During operation, the dialog manager system 406 presents the different topics using a default summarization level that is stored with the user preference data.”; and [0082] … the in-vehicle infotainment system 600 transmits the summarization as text or as text to speech audio data that are encoded in an audio format, such as MP3, to the mobile device 670 for presentation through the mobile device. The user optionally enters input through the multi-modal input devices in the in-vehicle infotainment system 600 to request a version of the summary that includes an increased or decreased level of detail in comparison to the default level of detail for the summarized presentation. The dialog manager system 406 receives the requests and selects a version of the summary with the requested level of detail for presentation to the user.)”);
determining, from the data storage, third data corresponding to the entity (see ¶ [0003, 0009, and 0081-0082] citations as in limitation above: information stored in the domain knowledge (e.g., news content) database); and
presenting the second data or the third data (see ¶ [0003, 0009, and 0081-0082] citations as in limitation above, more specifically: “[0081]…dialog manager system 406 presents the different topics using a default summarization level that is stored with the user preference data”).
Nalage et al., Fan et al., and Weng et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in information summarization/copying. 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 Nalage et al. in combination with Fan et al. to incorporate the teachings of Weng et al. of receiving audio data representing a spoken natural language input corresponding to an entity; determining, from the data storage, the second data based on the second data corresponding to the entity; determining, from the data storage, third data corresponding to the entity; and presenting the second data or the third data which provides the benefit of providing information based on user preferences ([0073] of Weng et al.).
20. The computing system of claim 11, wherein the at least one memory further comprises instructions that, when executed by the at least one processor (see Figure 3 (System Architecture) of Nalage et al.), cause the system computing to:
[the limitation as in claim 10, above].
Conclusion
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Keisha Y. Castillo-Torres
Examiner
Art Unit 2659
/Keisha Y. Castillo-Torres/Examiner, Art Unit 2659
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