DETAILED ACTION
This examination is in response to the communication filed on 11/29/2024. Claims 1-17 are currently pending, where claims 1 and 8 are independent.
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 statements (IDS) submitted on 11/29/2024; 10/22/2025; and 12/10/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-17 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 2-7, 9 and 10 of U.S. Patent No. 12,189,696. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the ‘696 either teach all of the claimed elements or a combination/incorporation of multiple claims within the ‘696 patent would have been obvious to one having ordinary skill in the art. See the detailed element mapping below.
Present Application
‘696 Patent
Claim 1: A computer implemented method for improving a search engine comprising:
Claim 6: A computer implemented method for improving a search engine comprising:
a. receiving a text corpus;
a. receiving a text corpus;
b. determining a list of n-gram candidates, each being a series of consecutive words of said text corpus, a number of said consecutive words within said series being an integer n superior or equal to two;
b. determining a list of n-gram candidates, each being a series of consecutive words of said text corpus, a number of said consecutive words within said series being an integer n superior or equal to two;
c. modifying at least partially said text corpus based on said list of n-gram candidates;
c. modifying at least partially said text corpus based on said list of n-gram candidates;
d. performing a machine learning embedding on the text corpus at least partially modified in step c;
d. performing a machine learning embedding on the text corpus at least partially modified in step c;
e. for each element in said list of n-gram candidates, computing a score based on the embedding of said element and the embeddings of the words making up said element; and
e. for each element in said list of n-gram candidates, computing a score based on the embedding of said element and the embeddings of the words making up said element; and
f. adding one or more of the n-gram candidates to a search engine queries items list based on their respective scores,
f. adding one or more of the n-gram candidates to a search engine queries items list based on their respective scores,
wherein the number of said consecutive words within said series is equal to two, and step e includes computing a maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element.
wherein the number of said consecutive words within said series is equal to two, and step e includes computing a maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element.
Claim 2. The computer implemented method according to claim 1, wherein each time a series of consecutive words of said text corpus make up a given element of said list of n-gram candidates, replacing said series of consecutive words by a token associated with a corresponding given element.
Dependent Claim 6 teaches all of the element of independent claim 1 of the instant application (see detailed element mapping above).
In addition, Claim 2 further teaches wherein each time a series of consecutive words of said text corpus make up a given element of said list of n-gram candidates, replacing said series of consecutive words by a token associated with a corresponding given element.
Claim 3. The computer implemented method according to claim 2, wherein step c further includes, upon identifying a series of consecutive words of said text corpus making up a given element of said list of n-gram candidates, determining whether one or more words consecutive to said series of consecutive words of said text corpus make up a different element of said list of n-gram candidates with one or more of endmost words of said series of consecutive words of said text corpus, and, in such case, duplicating the series of consecutive words making up said different element and replacing said series of consecutive words by a token associated with said different element.
Dependent Claim 6 teaches all of the element of independent claim 1 of the instant application (see detailed element mapping above).
In addition, Claim 3 further teaches wherein step c further includes, upon identifying a series of consecutive words of said text corpus making up a given element of said list of n-gram candidates, determining whether one or more words consecutive to said series of consecutive words of said text corpus make up a different element of said list of n-gram candidates with one or more of endmost words of said series of consecutive words of said text corpus, and, in such case, duplicating the series of consecutive words making up said different element and replacing said series of consecutive words by a token associated with said different element.
Claim 4. The computer implemented method according to claim 1, wherein step c includes copying the text corpus such that each word of the text corpus appears a number of times equal to the number of said consecutive words within said series being an integer n superior or equal to two, and parsing each copy of the text corpus by analyzing each sentence by series of consecutive words which number is the integer n superior or equal to two, each sentence of a copy being parsed with an offset, the offset being different for each copy.
Dependent Claim 6 teaches all of the element of independent claim 1 of the instant application (see detailed element mapping above).
In addition, Claim 4 further teaches step c includes copying the text corpus such that each word of the text corpus appears a number of times equal to the number of said consecutive words within said series being an integer n superior or equal to two, and parsing each copy of the text corpus by analyzing each sentence by series of consecutive words which number is the integer n superior or equal to two, each sentence of a copy being parsed with an offset, the offset being different for each copy.
Claim 5. The computer implemented method according to claim 1, wherein step e includes computing cosine distance or Euclidian distance between the embedding of said element and embeddings of the words making up said element.
Dependent Claim 6 teaches all of the element of independent claim 1 of the instant application (see detailed element mapping above).
In addition, Claim 5 further teaches wherein step e includes computing cosine distance or Euclidian distance between the embedding of said element and embeddings of the words making up said element.
Claim 8: A computer implemented method for improving a search engine comprising:
Claim 6: A computer implemented method for improving a search engine comprising:
a. receiving a text corpus;
a. receiving a text corpus;
b. determining a list of n-gram candidates, each being a series of consecutive words of said text corpus, a number of said consecutive words within said series being an integer n superior or equal to two;
b. determining a list of n-gram candidates, each being a series of consecutive words of said text corpus, a number of said consecutive words within said series being an integer n superior or equal to two;
c. modifying at least partially said text corpus based on said list of n-gram candidates;
c. modifying at least partially said text corpus based on said list of n-gram candidates;
d. performing a machine learning embedding on the text corpus at least partially modified in step c;
d. performing a machine learning embedding on the text corpus at least partially modified in step c;
e. for each element in said list of n-gram candidates, computing a score based on the embedding of said element and the embeddings of the words making up said element; and
e. for each element in said list of n-gram candidates, computing a score based on the embedding of said element and the embeddings of the words making up said element; and
f. adding one or more of the n-gram candidates to a search engine queries items list based on their respective scores,
f. adding one or more of the n-gram candidates to a search engine queries items list based on their respective scores,
wherein the number of said consecutive words within said series is equal to three, and step e includes computing a maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element.
wherein the number of said consecutive words within said series is equal to two, and step e includes computing a maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element.
In addition, Claim 7 further teaches the number of said consecutive words within said series is equal to three,
Claim 6. The computer implemented method according to claim 1, wherein the number of said consecutive words within said series is equal to three, and step e includes computing a maximum of a distance between the embedding of said element and a sum of embedding of two consecutive words of the words making up a given element and embedding of remaining word making up said element.
Dependent Claim 6 teaches all of the element of independent claim 1 of the instant application (see detailed element mapping above).
In addition, Claim 7 further teaches the number of said consecutive words within said series is equal to three, and step e includes computing a maximum of a distance between the embedding of said element and a sum of embedding of two consecutive words of the words making up a given element and embedding of remaining word making up said element
Claim 7. The computer implemented method according to claim 1, wherein step b includes using pointwise mutual information between words.
Dependent Claim 6 teaches all of the element of independent claim 1 of the instant application (see detailed element mapping above).
In addition, Claim 8 further teaches step b includes using pointwise mutual information between words.
Claim 9. The computer implemented method according to claim 8, wherein each time a series of consecutive words of said text corpus make up a given element of said list of n-gram candidates, replacing said series of consecutive words by a token associated with a corresponding given element.
Dependent Claims 6 and 7 teaches all of the element of independent claim 8 of the instant application (see detailed element mapping above).
In addition, Claim 2 further teaches wherein each time a series of consecutive words of said text corpus make up a given element of said list of n-gram candidates, replacing said series of consecutive words by a token associated with a corresponding given element.
Claim 10. The computer implemented method according to claim 9, wherein step c further includes, upon identifying a series of consecutive words of said text corpus making up a given element of said list of n-gram candidates, determining whether one or more words consecutive to said series of consecutive words of said text corpus make up a different element of said list of n-gram candidates with one or more of endmost words of said series of consecutive words of said text corpus, and, in such case, duplicating the series of consecutive words making up said different element and replacing said series of consecutive words by a token associated with said different element.
Dependent Claims 6 and 7 teaches all of the element of independent claim 8 of the instant application (see detailed element mapping above).
In addition, claim 3 further teaches step c further includes, upon identifying a series of consecutive words of said text corpus making up a given element of said list of n-gram candidates, determining whether one or more words consecutive to said series of consecutive words of said text corpus make up a different element of said list of n-gram candidates with one or more of endmost words of said series of consecutive words of said text corpus, and, in such case, duplicating the series of consecutive words making up said different element and replacing said series of consecutive words by a token associated with said different element.
Claim 11. The computer implemented method according to claim 8, wherein step c includes copying the text corpus such that each word of the text corpus appears a number of times equal to the number of said consecutive words within said series being an integer n superior or equal to two, and parsing each copy of the text corpus by analyzing each sentence by series of consecutive words which number is the integer n superior or equal to two, each sentence of a copy being parsed with an offset, the offset being different for each copy.
Dependent Claims 6 and 7 teaches all of the element of independent claim 8 of the instant application (see detailed element mapping above).
In addition, claim 4 further teaches step c includes copying the text corpus such that each word of the text corpus appears a number of times equal to the number of said consecutive words within said series being an integer n superior or equal to two, and parsing each copy of the text corpus by analyzing each sentence by series of consecutive words which number is the integer n superior or equal to two, each sentence of a copy being parsed with an offset, the offset being different for each copy.
Claim 12. The computer implemented method according to claim 8, wherein step e includes computing cosine distance or Euclidian distance between the embedding of said element and embeddings of the words making up said element.
Dependent Claims 6 and 7 teaches all of the element of independent claim 8 of the instant application (see detailed element mapping above).
In addition, claim 5 further teaches step e includes computing cosine distance or Euclidian distance between the embedding of said element and embeddings of the words making up said element.
Claim 13. The computer implemented method according to claim 8, wherein the number of said consecutive words within said series is equal to three, and step e includes computing a maximum of a distance between the embedding of said element and a sum of embedding of two consecutive words of the words making up a given element and embedding of remaining word making up said element.
Dependent Claims 6 and 7 teaches all of the element of independent claim 8 of the instant application (see detailed element mapping above).
In addition, claim 7 further teaches the number of said consecutive words within said series is equal to three, and step e includes computing a maximum of a distance between the embedding of said element and a sum of embedding of two consecutive words of the words making up a given element and embedding of remaining word making up said element.
Claim 14. The computer implemented method according to claim 8, wherein step b includes using pointwise mutual information between words.
Dependent Claims 6 and 7 teaches all of the element of independent claim 8 of the instant application (see detailed element mapping above).
In addition, claim 8 further teaches step b includes using pointwise mutual information between words.
Claim 15. A non-transitory computer readable medium having stored thereon a computer program comprising instructions for performing the method for improving the search engine according to claim 1.
Claim 9 teaches a non-transitory computer readable medium having stored there a computer program comprising instructions for performing a method for improving a search engine including all of the steps of claim 1. In addition, claim 6 further teaches wherein the number of said consecutive words within said series is equal to two, and step e includes computing a maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element
Claim 16. A non-transitory computer readable medium having stored thereon a computer program comprising instructions for performing the method for improving the search engine according to claim 8.
Claim 9 teaches a non-transitory computer readable medium having stored there a computer program comprising instructions for performing a method for improving a search engine including all of the steps of claim 1. In addition, claim 6 further teaches wherein the number of said consecutive words within said series is equal to two, and step e includes computing a maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element, and claim 7 further teaches the number of said consecutive words within said series is equal to three.
Claim 17: A computer system implementing a search engine comprising:
Claim 10: A computer system implementing a search engine comprising:
a processor coupled to a memory, the memory having recorded thereon a program comprising instructions for improving the search engine that when executed by the processor causes the processor to be configured to:
a processor coupled to a memory, the memory having recorded thereon a program comprising instructions for improving the search engine that when executed by the processor causes the processor to be configured to:
a. receive a text corpus;
a. receive a text corpus;
b. determine a list of n-gram candidates, each being a series of consecutive words of said text corpus, a number of said consecutive words within said series being an integer n superior or equal to two;
b. determine a list of n-gram candidates, each being a series of consecutive words of said text corpus, a number of said consecutive words within said series being an integer n superior or equal to two;
c. modify at least partially said text corpus based on said list of n-gram candidates;
c. modify at least partially said text corpus based on said list of n-gram candidates;
d. perform a machine learning embedding on the text corpus at least partially modified in step c;
d. perform a machine learning embedding on the text corpus at least partially modified in step c;
e. for each element in said list of n-gram candidates, compute a score based on the embedding of said element and the embeddings of the words making up said element; and
e. for each element in said list of n-gram candidates, compute a score based on the embedding of said element and the embeddings of the words making up said element; and
f. add one or more of the n-gram candidates to a search engine queries items list based on their respective scores,
f. add one or more of the n-gram candidates to a search engine queries items list based on their respective scores,
wherein the number of said consecutive words within said series is equal to two, and step e includes computing a maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element.
Claim 6 further teaches wherein the number of said consecutive words within said series is equal to two, and step e includes computing a maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element.
Allowable Subject Matter
Claim 1-17 would be allowable if rewritten or amended to overcome the double patenting rejections set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding independent claims 1 and 8, He teaches a computer implemented method for improving search engine queries ( Fig. 2 and ¶ [0036] teaches the pre-processing module 62 can be a separate processor within server 54 or carried out using the processor 68); a non-transitory computer readable medium having stored thereon a computer program comprising instructions for performing a method for improving search engine queries (Fig. 2 and ¶[0037] teaches the memory storage unit 64 is configured to store codes for directing the processor 68 for carrying out computer implemented methods. For example, the codes can include the programming instructions); and a processor coupled to a memory, the memory having recorded thereon a program comprising instructions for improving search engine queries that when executed by the processor causes the processor to be configured to (Fig. 2 and ¶[0037] teaches the system includes the memory storage unit 64 is configured to store codes for directing the processor 68 for carrying out computer implemented methods. For example, the codes can include the programming instructions), respectively, comprising:
a. receiving a text corpus (Fig. 3 and ¶[0041] teaches receiving an input query, e.g., text string, having a plurality of words at the server 54);
b. determining a list of n-gram candidates, each being a series of consecutive words of said text corpus, a number of said consecutive words within said series being an integer n superior or equal to two(¶[0043] teaches assigning a first n-gram and a different second n-gram to the plurality of words in the input query. Specifically, the pre-processing module 62 identifies all possible bigrams, trigrams, four-grams, five-grams, etc. based on information in a background corpus, where the first n-gram comprises only unigrams and the second n-gram comprises bigrams);
c. modifying at least partially said text corpus based on said list of n-gram candidates (¶[0044] teaches the pre-processing module 62 would assign a first n-gram comprising individual words: I, want, to, buy, x, box, 360, at, best, buy. The pre-processing module 62 can also assign a second n-gram comprising bigrams, such as: I_want, want_to, to, buy, x_box, box_360, at, best buy. Assigning the second n-gram is interpreted as modifying the text corpus, i.e., text query);
d. performing a machine learning embedding on a resulting text corpus (¶[0019] teaches converting the sequences, i.e., n-grams, into a format usable by a recurrent neural network, such as a vector format. This conversion can be performed using embeddings, e.g., a mapping from strings to vectors).
He fails to explicitly disclose e. for each element in said list of n-gram candidates, computing a score based on the embedding of said element and the embeddings of the words making up said element; and f. adding one or more of the n-gram candidates to a search engine queries items list based on their respective scores.
Wang discloses an embedding-based parsing of search queries that includes parsing the query to identify a subset of n-grams; generating, for each identified n-gram, an embedding of the n-gram; determining, for each identified n-gram, one or more word senses; calculating, for each word sense for each identified n-gram, a relatedness-score for the word sense based similarity metrics of the embedding of the word sense and the embeddings of each of the other word senses corresponding to the other identified n-grams; selecting, for each identified n-gram, one of the word senses determined for the identified n-gram having a highest relatedness-score; identifying objects matching at least a portion of the query (Wang, Abstract).
Regarding the relatedness-score, col. 19, ll. 24-57 teaches each n-gram may have multiple related word senses, where a word sense of first n-gram may refer to a second n-gram representing a concept or a meaning of the first n-gram. In addition, col. 23, l. 60 to col. 23, l. 36 teaches calculating relatedness-scores comprises summing an initial-score for the word sense and a context-score or the word sense, wherein the context-score may be a sum of the one or more similarity metrics of the embedding of the word sense and the embedding of each of the one or more other word senses corresponding to the other identified n-grams, respectively. Where the context-score of a word sense may represent how likely a word sense is the intended meaning of the corresponding identified n-gram given the other identified n-grams.
Thus, Wang teaches e. for each element in said list of n-gram candidates, computing a score (i.e., the relatedness-score) based on the embedding of said element (i.e., n-gram) and the embeddings of the words making up said element (i.e., the other identified n-grams); and f. adding one or more of the n-gram candidates to a search engine queries items list based on their respective scores.
However, the combination of He and Wang fails to disclose or suggest that the relatedness score comprises calculating a “maximum of a distance between the embedding of said element and a sum of the embeddings of the words making up a given element, and the distance between the embedding of said element and each separate embedding of the words making up said element” as recited in independent claims 1 and 8. Further, there is not rationale for one having ordinary skill in the art to modify the system taught by He and Wang to calculate a maximum distance as claimed.
Claims 2-7 and 9-17 either depend from or incorporate the subject matter of claim 1 or 8. Therefore, claims 2-7 and 9-17 are patentable over the prior art of record for at least those reasons presented above with respect to claims 1 and 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Eberle et al. (US 2023/0342551) teaches methods and systems for providing user input recommendations;
Cai et al. “Knowledge-Enhanced Multi-semantic Fusion for Concept Similarity Measurement in Continuous Vector Space” teaches method and system which to capture and understand latent semantics of words, retrofits the semantic features in distributed representations and carries out a sense per word; and
Barba et al. (US 9336192) teaches methods for analyzing text.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PENNY L CAUDLE whose telephone number is (703)756-1432. The examiner can normally be reached M-Th 8:00 am to 5:00 pm eastern.
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/PENNY L CAUDLE/Examiner, Art Unit 2657
/DANIEL C WASHBURN/Supervisory Patent Examiner, Art Unit 2657
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