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 .
DETAILED ACTION
1. This action is responsive to the communication filed on 9/4/2026. Claims 51-54 and 61-64 have been amended. Claim 70 has been cancelled. Claim 71 has been added. Claims 51-69 and 71 are pending.
Applicants' arguments filed 9/4/26 have been fully considered but they are not deemed to be persuasive. Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Specification
2. The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Newly added claims 51 and 61 recite the limitations “wherein the data indicative of the set of filler tokens is stored in the memory prior to receiving the first query and the second query”. The limitation is not clearly defined in the Applicant’s specification.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The following is a quotation of the first paragraph of 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
3. Newly added claims 51-69 and 71 are rejected under 35 U.S.C. 112, first paragraph, as containing subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
The original disclosure does not contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art that it pertains the limitation “wherein the data indicative of the set of filler tokens is stored in the memory prior to receiving the first query and the second query” in the newly added claims 51 and 61 are considered to be new matter.
Further, as stated on page 8 of Applicant’s remarks and arguments presented on September 04, 2026 “As stated at paragraph [0005] of Applicant's Specification, "an interactive media guidance application, implemented on control circuitry, receives a first query and a second query ... The interactive media guidance application isolates each query into a plurality of tokens." As shown in FIG. 3, a non-limiting example of such first and second queries may be "Give me Some Beethoven Movies" and "I Meant the Dog," respectively, where each of the first query and the second query may be isolated into a plurality of tokens ( e.g., to obtain a first set of tokens and a second set of tokens).” Clearly, filter tokens are not stored in the memory prior to receiving the first query and the second query.
Dependent claims are rejected for inheriting the deficiencies of the base claims.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The following is a quotation of the second paragraph of 35 U.S.C. 112:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
4. Newly added claims 51-69 and 71 are rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claims 51 and 61 recite the limitation “wherein the data indicative of the set of filler tokens is stored in the memory prior to receiving the first query and the second query” respectively, but there is no clear definition of the limitation in the instant applicant’s specification.
Further, as stated on page 8 of Applicant’s remarks and arguments presented on September 04, 2026 “As stated at paragraph [0005] of Applicant's Specification, "an interactive media guidance application, implemented on control circuitry, receives a first query and a second query
... The interactive media guidance application isolates each query into a plurality of tokens." As shown in FIG. 3, a non-limiting example of such first and second queries may be "Give me Some Beethoven Movies" and "I Meant the Dog," respectively, where each of the first query and the second query may be isolated into a plurality of tokens ( e.g., to obtain a first set of tokens and a second set of tokens).” Clearly, filter tokens are not stored in the memory prior to receiving the first query and the second query.
Therefore, claims 51 and 61 are indefinite.
Dependent claims are rejected for inheriting the deficiencies of the base claims.
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.
5. Claims 51-69 and 71 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the guidance set forth in MPEP 2106 which has incorporated the 2019 PEG.
Regarding to claim 51,
Step 1 Analysis: Claim 51 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 51 recites: A method comprising:
“receiving a first query and a second query, wherein the first query is received prior to the second query and a response to the second query has not yet been provided”. This element reads on a person receives a first query and a second query, wherein the first query is received prior to the second query and a response to the second query has not yet been provided which could be considered a mental process of an observation or evaluation.
“generating a first set of tokens based on one or more portions of the first query”. This element reads on a person generates a first set of tokens based on one or more portions of the first query which could be considered a mental process of an observation or evaluation.
“generating a second set of tokens based on one or more portions of the second query”. This element reads on a person generates a second set of tokens based on one or more portions of the second query which could be considered a mental process of an observation or evaluation.
“retrieving data indicative of a set of filler tokens, wherein the data indicative of the set of filler tokens is stored prior to receiving the first query and the second query”. This element reads on a person retrieves data indicative of a set of filler tokens, wherein the data indicative of the set of filler tokens is stored prior to receiving the first query and the second query which could be considered a mental process of an observation or evaluation.
“generating an updated first set of tokens based at least in part on determining which tokens of the first set of tokens generated for the first query are not associated with the set of filler tokens”. This element reads on a person generates an updated first set of tokens based at least in part on determining which tokens of the first set of tokens generated for the first query are not associated with the set of filler tokens which could be considered a mental process of an observation or evaluation.
“generating an updated second set of tokens based at least in part on determining which tokens of the second set of tokens generated for the second query are not associated with the set of filler tokens”. This element reads on a person generates an updated second set of tokens based at least in part on determining which tokens of the second set of tokens generated for the second query are not associated with the set of filler tokens which could be considered a mental process of an observation or evaluation.
“using a machine learning model to process data indicative of the updated first set of tokens and data indicative of the updated second set of tokens”. This element reads on a person uses a machine learning model to process data indicative of the updated first set of tokens and data indicative of the updated second set of tokens which could be considered a mental process of an observation or evaluation.
“based at least in part on the use of the machine learning model to perform the processing, receiving an output from the machine learning model”. This element reads on a person based at least in part on the use of the machine learning model to perform the processing, receiving an output from the machine learning model which could be considered a mental process of an observation or evaluation.
“generating for output the response to the second query, based at least part on the output of the machine learning model”. This element reads on a person generates for output the response to the second query, based at least part on the output of the machine learning model which could be considered a mental process of an observation or evaluation.
Overall, the limitations directed to generate output based on a first query and a second query and the various mental process limitations in the context of this claim encompasses limitations that are not only considered to be directed to limitations that could be practically performed in the human mind (including observations and preform an evaluation, judgment, and opinion) aided by the use of pen and paper. If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitation in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: In Step 2A Prong 2, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether they integrate the exception into a practical application of the exception.
In particular, the claim only recites the additional elements of “a computer- implemented” and “memory.”
Regarding the computer- implemented,
The processor of a computer system for generating and storing in all steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (generating and storing). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding the memory,
The memory of a computer system for generating and storing in all steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (generating and storing). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional elements “a computer- implemented” and “memory” are simply applying the abstract idea, and there is nothing done with results. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, and does not provide any improvement in computer technology (see MPEP2106.05(a)).
Therefore, the additional elements do not integrate the judicial exception into a practical application.
Step 2B Analysis: In Step 2B, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether the additional elements, taken individually and in combination, result in the claim as a whole amounting to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, The additional elements “a computer- implemented” and “memory” are simply applying the abstract idea, and there is nothing done with results.
Accordingly, this additional element(s), taken individually and in combination, do not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 52,
Step 1 Analysis: Claim 52 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 52 is dependent on claim 51, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 52 recites “
generating the updated first set of tokens comprises excluding a first one or more filler tokens of the first query, wherein each of the first one or more filler tokens are comprised in the set of filler tokens, and wherein the updated first set of tokens includes at least one token from the first set of tokens; and
generating the updated second set of tokens comprises excluding a second one or more filler tokens of the second query, wherein each of the second one or more filler tokens are comprised in the set of filler tokens, and wherein the updated second set of tokens includes at least one token from the second set of tokens
." That is, the claim recites
generating the updated first set of tokens comprises excluding a first one or more filler tokens of the first query, wherein each of the first one or more filler tokens are comprised in the set of filler tokens, and wherein the updated first set of tokens includes at least one token from the first set of tokens; and
generating the updated second set of tokens comprises excluding a second one or more filler tokens of the second query, wherein each of the second one or more filler tokens are comprised in the set of filler tokens, and wherein the updated second set of tokens includes at least one token from the second set of tokens. The above-noted limitation of claim 52, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 53,
Step 1 Analysis: Claim 53 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 53 is dependent on claim 51, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 53 recites “
determining one or more first probability values indicative of a respective probability that a first respective token of the updated first set of tokens corresponds to a first respective entity;
determining one or more second probability values indicative of a respective probability that a second respective token of the updated second set of tokens corresponds to a second respective entity; and
the use of the machine learning model to perform the processing further comprises inputting data indicative of the one or more first probability values and data indicative of the one or more second probability values to the machine learning model." That is, the claim recites
determining one or more first probability values indicative of a respective probability that a first respective token of the updated first set of tokens corresponds to a first respective entity;
determining one or more second probability values indicative of a respective probability that a second respective token of the updated second set of tokens corresponds to a second respective entity; and
the use of the machine learning model to perform the processing further comprises inputting data indicative of the one or more first probability values and data indicative of the one or more second probability values to the machine learning model. The above-noted limitation of claim 53, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 54,
Step 1 Analysis: Claim 54 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 54 is dependent on claims 51&53, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 54 recites “
determining a first probability value of the one or more first probability values based on a first set of nodes of a knowledge graph, wherein the first set of nodes indicate a connection between the first respective token and the first respective entity; and
determining a second probability value of the one or more second probability values based on a second set of nodes of the knowledge graph, wherein the second set of nodes indicate a connection between the second respective token and the second respective entity." That is, the claim recites
determining a first probability value of the one or more first probability values based on a first set of nodes of a knowledge graph, wherein the first set of nodes indicate a connection between the first respective token and the first respective entity; and
determining a second probability value of the one or more second probability values based on a second set of nodes of the knowledge graph, wherein the second set of nodes indicate a connection between the second respective token and the second respective entity. The above-noted limitation of claim 54, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 55,
Step 1 Analysis: Claim 55 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 55 is dependent on claim 51, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 55 recites “wherein the output indicates either (i) existence of context change from the first query to the second query or (ii) no existence of context change from the first query to the second query." That is, the claim recites wherein the output indicates either (i) existence of context change from the first query to the second query or (ii) no existence of context change from the first query to the second query. The above-noted limitation of claim 55, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 56,
Step 1 Analysis: Claim 56 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 56 is dependent on claim 51, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 56 recites “modifying, based on the output indicating the merge continuity type, the updated second set of tokens to further comprise one or more tokens of the first set of tokens." That is, the claim recites modifying, based on the output indicating the merge continuity type, the updated second set of tokens to further comprise one or more tokens of the first set of tokens. The above-noted limitation of claim 56, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 57,
Step 1 Analysis: Claim 57 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 57 is dependent on claim 51, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 57 recites “replacing, based on the output indicating the replacement continuity type, one or more tokens of the updated second set of tokens with one or more tokens of the first query." That is, the claim recites replacing, based on the output indicating the replacement continuity type, one or more tokens of the updated second set of tokens with one or more tokens of the first query. The above-noted limitation of claim 57, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 58,
Step 1 Analysis: Claim 58 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 58 is dependent on claim 51, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 58 recites “updating, based on the output indicating the clarification continuity type, an entity type for at least one token of the updated second set of tokens based on an entity type for at least one token of the first set of tokens." That is, the claim recites updating, based on the output indicating the clarification continuity type, an entity type for at least one token of the updated second set of tokens based on an entity type for at least one token of the first set of tokens. The above-noted limitation of claim 58, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 59,
Step 1 Analysis: Claim 59 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 59 is dependent on claim 51, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 59 recites “wherein the machine learning model is trained on a training dataset comprising (i) multiple previous-next query pairs where context is preserved and (ii) multiple previous-next query pairs where context is not preserved." That is, the claim recites wherein the machine learning model is trained on a training dataset comprising (i) multiple previous-next query pairs where context is preserved and (ii) multiple previous-next query pairs where context is not preserved. The above-noted limitation of claim 59, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 60,
Step 1 Analysis: Claim 60 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 60 is dependent on claim 51, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 60 recites “each of the one or more portions of the first query is comprised of a respective word of the first query; and each of the one or more portions of the second query is comprised of a respective word of the second query." That is, the claim recites each of the one or more portions of the first query is comprised of a respective word of the first query; and each of the one or more portions of the second query is comprised of a respective word of the second query. The above-noted limitation of claim 60, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 61-69 and 71 are rejected under 35 U.S.C. 101 with the same rational of claims 51-60.
Claim Rejections - 35 USC § 103
6. 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 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.
7. 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.
8. 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.
9. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
10. Claims 51-53, 56-57, 60-63, 66-67 and 71 are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan et al (US 20090119248 A1, hereinafter “Sundaresan”) in view of Sarikaya et al (U.S. Patent 20150142420 A1 hereinafter, “Sarikaya”).
11. With respect to claim 51,
Sundaresan discloses a computer-implemented method comprising:
receiving a first query and a second query, wherein the first query is received prior to the second query;
generating a first set of tokens based on one or more portions of the first query;
generating a second set of tokens based on one or more portions of the second query;
retrieving, from memory, data indicative of a set of filler tokens, wherein the data indicative of the set of filler tokens is stored in the memory prior to receiving the first query and the second query;
generating an updated first set of tokens based at least in part on determining which tokens of the first set of tokens generated for the first query are not associated with the set of filler tokens;
generating an updated second set of tokens based at least in part on determining which tokens of the second set of tokens generated for the second query are not associated with the set of filler tokens;;
using a model to process data indicative of the updated first set of tokens and data indicative of the updated second set of tokens;
based at least in part on the use of the model to perform the processing, receiving an output from the model; and
generating for output the response to the second query (Sundaresan [0024] – [0026], [0032] and Fig. 3 e.g. [0024] These other previously submitted queries include query terms and a diversity of these query terms is measured at 306. In an illustrative embodiment, the diversity of the query terms may be measured by calculating a diversity index. A diversity index is a statistic that is normally used to measure a biodiversity of an ecosystem. However, the diversity index may be additionally applied to measure the diversity of query terms or other data associated with a search, in accordance with embodiments of the present invention. For example, the diversity index may be embodied as a Simpson's diversity index "D" expressed as: D = S∑i=1 ni(ni-1 ) ∕ N(N-1) where S is the number of species, N is the total percentage cover or total number of organisms, and n is the percentage cover of a species or number of organisms of a species. Here, the number of species S, total percentage cover N, or the percentage cover of a species n may be substituted with a variety of suitable data associated with a search. For example, the total percentage cover N may be substituted with a total number of query terms and the percentage cover of a species n may be substituted with each of the query terms. [0025] In addition, the diversity index may be embodied as Shannon index "H" expressed as: H' = -S∑i=1pilnpi where S is the number of species and pi is the relative abundance of each species defined as ni/N, where N is the total number of all individuals and ni is the number of individuals in a species. Similarly, the number of species S and the relative abundance of each species pi may be substituted with a variety of suitable data associated with a search. For example, the number of species S may be substituted with a total number of query terms and the number of individuals in a species ni may be substituted with each of the query terms. [0026] The search may then be expanded based on the measured diversity. In an embodiment, the search may be expanded by expanding the query with the other previously submitted query terms at 308. For example, previous submitted queries related to videogames include query terms "role-playing," "action," "simulation," and "sports" can be added to or combined with a query "fun video games" to create a new, expanded query "role-playing action simulation sports fun videogames." As explained in more detail below, the decision of whether to expand the search is based on the measured diversity. [0032] The diversity of the other previously submitted queries is then measured by, for example, calculating a diversity index of the query terms at 508. This calculated diversity index is then compared to a threshold value at 510, which may be a predefined value defining a minimum level of diversity. If the diversity index is less than the threshold value, then the other previously submitted query terms are not sufficiently diverse and therefore, are not used to expand the search. On the other hand, if the diversity index is greater than the threshold value, then these other previously submitted query terms are sufficiently diverse and may be used to expand the search. In an example embodiment, the search may be expanded by adding these previously submitted query terms to the query at 512, which is discussed above. In an alternate embodiment, the search may be expanded by submitting these other previously submitted query terms instead of the original query terms for the search. For example, previous submitted queries related to "sofas" include query terms "furniture" and "couch." Instead of using the original query "sofas" for the search, the query "furniture couch" may replace the original query "sofas" and be used in the search [as
receiving a first query (Sundaresan e.g. other previously submitted search) and a second query (Sundaresan e.g. the search), wherein the first query is received prior to the second query;
generating a first set of tokens based on one or more portions of the first query (Sundaresan e.g. terms of other previously summitted search);
generating a second set of tokens based on one or more portions of the second query (Sundaresan e.g. terms of the search);
retrieving, from memory, data indicative of a set of filler tokens (Sundaresan e.g. diversity terms; referring to the instant applicant’s specification [0003] and/or [0012]), wherein the data indicative of the set of filler tokens is stored in the memory prior to (Sundaresan e.g. Diversity terms were received and stored based on previously submitted prior to other previously submitted and the query) receiving the first query and the second query;
generating an updated first set of tokens (Sundaresan e.g. not diversity terms of other previously summitted search) based at least in part on determining which tokens of the first set of tokens generated for the first query (Sundaresan e.g. other previously submitted search) are not associated with the set of filler tokens (Sundaresan e.g. diversity terms);
generating an updated second set of tokens (Sundaresan e.g. not diversity terms of the search) based at least in part on determining which tokens of the second set of tokens generated for the second query (Sundaresan e.g. the search) are not associated with the set of filler tokens (Sundaresan e.g. diversity terms);
using a model to process data indicative of the updated first set of tokens and data indicative of the updated second set of tokens;
based at least in part on the use of the machine learning model to perform the processing; and
generating for output the response to the second query]), based at least part on the output of the model.
Although Iyer substantially teaches the claimed invention, Iyer does not explicitly indicate
a response to the second query has not yet been provided;
a machine learning model;
receiving an output from the machine learning model;
based at least in part on the output of the machine learning model.
Sarikaya teaches the limitations by stating
a response to the second query has not yet been provided (Sarikaya [0026] – [0028], [0030], [0044] – [0045], [0049] and Figs. 1-2 e.g. see Fig. 1 - the word 110 "action" is inserted as a subject 214 into a slot 212 of the previous dialogue hypothesis 204 "show me movies," – merge “show me movies” with “action” into “show me action movies” [as receiving a first query (e.g. “show me movies”) and a second query (e.g. “action”), wherein the first query is received prior to the second query and a response to the second query has not yet been provided (e.g. see Fig. 1)]);
using a machine learning model to process data indicative of the updated first set of tokens and data indicative of the updated second set of tokens (Sarikaya [0024], [0044] e.g. machine-based learning);
based at least in part on the use of the machine learning model to perform the processing, receiving an output from the machine learning model (Sarikaya [0024], [0044] e.g. machine-based learning); and
generating for output the response to the second query, based at least part on the output of the machine learning model (Sarikaya [0024], [0044] e.g. machine-based learning).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Sundaresan and Sarikaya, to enable online shopping and auction websites effectively connect the buyers to the sellers (Sundaresan [0004]).
12. With respect to claim 52,
Sundaresan discloses
generating the updated first set of tokens comprises excluding a first one or more filler tokens of the first query, wherein each of the first one or more filler tokens are comprised in the set of filler tokens (Sundaresan e.g. not diversity terms of other previously summitted search) , and wherein the updated first set of tokens includes at least one token from the first set of tokens (Sundaresan e.g. Diversity terms were received, updated and stored based on previously submitted prior to other previously submitted and the query); and
generating the updated second set of tokens comprises excluding a second one or more filler tokens of the second query, wherein each of the second one or more filler tokens are comprised in the set of filler tokens (Sundaresan e.g. not diversity terms of the search), and wherein the updated second set of tokens includes at least one token from the second set of tokens (Sundaresan e.g. Diversity terms were received, updated and stored based on previously submitted prior to other previously submitted and the query).
13. With respect to claim 53,
Sundaresan discloses
determining one or more first probability values indicative of a respective probability that a first respective token of the updated first set of tokens corresponds to a first respective entity;
determining one or more second probability values indicative of a respective probability that a second respective token of the updated second set of tokens corresponds to a second respective entity; and
the use of the machine learning model to perform the processing further comprises inputting data indicative of the one or more first probability values and data indicative of the one or more second probability values to the machine learning model (Sundaresan [0030], [0037] e.g. probability).
14. With respect to claim 56,
Sarikaya discloses modifying, based on the output indicating the merge continuity type, the updated second set of tokens to further comprise one or more tokens of the first set of tokens (Sarikaya [0026] – [0028], [0030], [0044] – [0045], [0049] and Figs. 1-2 e.g. see Fig. 1 - the word 110 "action" is inserted as a subject 214 into a slot 212 of the previous dialogue hypothesis 204 "show me movies," – merge “show me movies” with “action” into “show me action movies”).
15. With respect to claim 57,
Sundaresan discloses replacing, based on the output indicating the replacement continuity type, one or more tokens of the updated second set of tokens with one or more tokens of the first query (Sundaresan [0024] – [0025], [0032] and Fig. 3 e.g. substituted).
16. With respect to claim 60,
Sundaresan discloses
each of the one or more portions of the first query is comprised of a respective word of the first query; and
each of the one or more portions of the second query is comprised of a respective word of the second query (Sundaresan [0016] e.g. words).
17. Claims 61-63 and 66-67 are same as claims 51-53 and 66-67 and are rejected for the same reasons as applied hereinabove.
18. With respect to claim 71,
Sundaresan discloses wherein the machine learning model comprises a first initial layer and a second initial layer, and wherein:
generating the updated first set of tokens comprises using the first initial layer to obtain the updated first set of tokens from the first set of tokens; and
generating the updated second set of tokens comprises using the second initial layer to obtain the updated second set of tokens from the second set of tokens (Sundaresan e.g. Diversity terms were received, updated and stored based on previously submitted prior to other previously submitted and the query).
19. Claims 54, 58, 64 and 68 are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan in view of Sarikaya, and further in view of London (U.S. 20150142704 A1 hereinafter, “London”).
20. With respect to claim 54,
Although Sundaresan and Sarikaya combination substantially teaches the claimed invention, they do not explicitly indicate
determining a first probability value of the one or more first probability values based on a first set of nodes of a knowledge graph, wherein the first set of nodes indicate a connection between the first respective token and the first respective entity; and
determining a second probability value of the one or more second probability values based on a second set of nodes of the knowledge graph, wherein the second set of nodes indicate a connection between the second respective token and the second respective entity.
London teaches the limitations by stating
determining a first probability value of the one or more first probability values based on a first set of nodes of a knowledge graph, wherein the first set of nodes indicate a connection between the first respective token and the first respective entity; and
determining a second probability value of the one or more second probability values based on a second set of nodes of the knowledge graph, wherein the second set of nodes indicate a connection between the second respective token and the second respective entity (London [0022] – [0024], [0140], [0198] – [0199] e.g. knowledge-tree graph).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Sundaresan, Sarikaya and London, to enable online shopping and auction websites effectively connect the buyers to the sellers (Sundaresan [0004]).
21. With respect to claim 58,
London discloses updating, based on the output indicating the clarification continuity type, an entity type for at least one token of the updated second set of tokens based on an entity type for at least one token of the first set of tokens (London claim 1, [0245] e.g. a type of entity; types of entities).
22. Claims 64 and 68 are same as claims 54 and 58 and are rejected for the same reasons as applied hereinabove.
23. Claims 55, 59, 65 and 69 are rejected under 35 U.S.C. 103 as being unpatentable over Sundaresan in view of Sarikaya, and further in view of Iyer et al (U.S. 20110131205 A1 hereinafter, “London”).
24. With respect to claim 55,
Although Sundaresan and Sarikaya combination substantially teaches the claimed invention, they do not explicitly indicate
wherein the output indicates either (i) existence of context change from the first query to the second query or (ii) no existence of context change from the first query to the second query.
Iyer teaches the limitations by stating wherein the output indicates either (i) existence of context change from the first query to the second query or (ii) no existence of context change from the first query to the second query (Iyer [0059] – [0063] e.g. [0059] Pairs of an original query, q1, and a rewritten query, q2, may be annotated from an assessment by annotators as one of four match types: Precise Match [as no existence], Approximate Match, Marginal Match and Clear Mismatch [as existence of context change]. In an embodiment, this may be simplified by mapping the four categories of match type into two categories, where the first two categories, Precise Match and Approximate Match, correspond to a "match" and the last two categories, Marginal Match and Clear Mismatch, correspond to a mismatch. [0060] At step 906, a match type score may be assigned for each category of match type for each query pair in the training sets of query pairs of an original query and a rewritten query. For example, a match type score of 0, 0.3, 0.7, and 1.0 may be respectively assigned for categories of Clear Mismatch, Marginal Match, Approximate Match and Precise Match. [0062] And a regression-based machine learning model maybe trained with term importance weights assigned as query features to queries of the training sets of query pairs of an original query and a rewritten query with a match type score at step 1010).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Sundaresan, Sarikaya and Iyer, to enable online shopping and auction websites effectively connect the buyers to the sellers (Sundaresan [0004]).
25. With respect to claim 59,
Iyer discloses wherein the machine learning model is trained on a training dataset comprising (i) multiple previous-next query pairs where context is preserved and (ii) multiple previous-next query pairs where context is not preserved (Iyer [0059] – [0063] e.g. [0059] Pairs of an original query, q1, and a rewritten query, q2, may be annotated from an assessment by annotators as one of four match types: Precise Match [as no existence], Approximate Match, Marginal Match and Clear Mismatch [as existence of context change]. In an embodiment, this may be simplified by mapping the four categories of match type into two categories, where the first two categories, Precise Match and Approximate Match, correspond to a "match" and the last two categories, Marginal Match and Clear Mismatch, correspond to a mismatch. [0060] At step 906, a match type score may be assigned for each category of match type for each query pair in the training sets of query pairs of an original query and a rewritten query. For example, a match type score of 0, 0.3, 0.7, and 1.0 may be respectively assigned for categories of Clear Mismatch, Marginal Match, Approximate Match and Precise Match. [0062] And a regression-based machine learning model maybe trained with term importance weights assigned as query features to queries of the training sets of query pairs of an original query and a rewritten query with a match type score at step 1010 [as (i) multiple previous-next query pairs where context is preserved (e.g. precise match) and (ii) multiple previous-next query pairs where context is not preserved (e.g. clear mismatch)]).
26. Claims 65 and 69 are same as claims 55 and 59 and are rejected for the same reasons as applied hereinabove.
Response to Argument
27. On pages 9-10, Applicant alleges that the claims provide a technical solution to a technical problem…." To help address this technical problem, a technical solution, necessarily rooted in computer technology, is reflected in the claimed subject matter. For example, the claims relate to receiving two queries, identifying a set of filler tokens, updating sets of tokens for each query based on their respective associations with the filler tokens, and based on such data, providing a response to the query using a machine learning model. Such dynamic and flexible approach to provide the query output is a technical improvement over conventional rule-based systems. … Applicant submits that the claims cannot practically be performed in the human mind, nor with the use of pen and paper. For example, it is not practical for a person, even if armed with pen and paper, to "use a machine learning model to process data indicative of the updated first set of tokens and data indicative of the updated second set of tokens." For at least these reasons, Applicant submits that the amended claims recite subject matter that is patent-eligible. Withdrawal of the rejection under 35 U.S.C. § 101 is respectfully requested.
Examiner disagrees because:
There is nothing in the instant applicant’s specification stated “Such dynamic and flexible approach to provide the query output is a technical improvement over conventional rule-based systems”.
Further, claim 51 recites: A method comprising:
“receiving a first query and a second query, wherein the first query is received prior to the second query and a response to the second query has not yet been provided”. This element reads on a person receives a first query and a second query, wherein the first query is received prior to the second query and a response to the second query has not yet been provided which could be considered a mental process of an observation or evaluation.
“generating a first set of tokens based on one or more portions of the first query”. This element reads on a person generates a first set of tokens based on one or more portions of the first query which could be considered a mental process of an observation or evaluation.
“generating a second set of tokens based on one or more portions of the second query”. This element reads on a person generates a second set of tokens based on one or more portions of the second query which could be considered a mental process of an observation or evaluation.
“retrieving data indicative of a set of filler tokens, wherein the data indicative of the set of filler tokens is stored prior to receiving the first query and the second query”. This element reads on a person retrieves data indicative of a set of filler tokens, wherein the data indicative of the set of filler tokens is stored prior to receiving the first query and the second query which could be considered a mental process of an observation or evaluation.
“generating an updated first set of tokens based at least in part on determining which tokens of the first set of tokens generated for the first query are not associated with the set of filler tokens”. This element reads on a person generates an updated first set of tokens based at least in part on determining which tokens of the first set of tokens generated for the first query are not associated with the set of filler tokens which could be considered a mental process of an observation or evaluation.
“generating an updated second set of tokens based at least in part on determining which tokens of the second set of tokens generated for the second query are not associated with the set of filler tokens”. This element reads on a person generates an updated second set of tokens based at least in part on determining which tokens of the second set of tokens generated for the second query are not associated with the set of filler tokens which could be considered a mental process of an observation or evaluation.
“using a machine learning model to process data indicative of the updated first set of tokens and data indicative of the updated second set of tokens”. This element reads on a person uses a machine learning model to process data indicative of the updated first set of tokens and data indicative of the updated second set of tokens which could be considered a mental process of an observation or evaluation.
“based at least in part on the use of the machine learning model to perform the processing, receiving an output from the machine learning model”. This element reads on a person based at least in part on the use of the machine learning model to perform the processing, receiving an output from the machine learning model which could be considered a mental process of an observation or evaluation.
“generating for output the response to the second query, based at least part on the output of the machine learning model”. This element reads on a person generates for output the response to the second query, based at least part on the output of the machine learning model which could be considered a mental process of an observation or evaluation.
Overall, the limitations directed to generate output based on a first query and a second query and the various mental process limitations in the context of this claim encompasses limitations that are not only considered to be directed to limitations that could be practically performed in the human mind (including observations and preform an evaluation, judgment, and opinion) aided by the use of pen and paper. If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitation in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
28. On pages 10-12, Applicant alleges that the cited art fails to disclose at least these features of Applicant's amended claims. Sundaresan teaches expanding the query terms included in a search query based on the calculated diversity of query terms included in previous search queries, if the calculated diversity of the previously submitted query terms is above a threshold value. See Sundaresan paras. [0024] - [0026], [0032]. The claimed "filler tokens" are equated in the Office Action to the alleged disclosure in Sundaresan of "diversity terms" This would appear to be terms such as "furniture" or "couch," taken from a previous search query related to "sofa," when a current search term is "sofa." However, in order to identify the "diversity terms," the previous search query in Sundaresan with "furniture" or "couch" must be received and its results analyzed. On the other hand, Applicant's claim requires that the "set of filler tokens" to be "stored in the memory prior to receiving the first query and the second query.".
Examiner disagrees because:
Diversity terms were received and stored based on previously submitted prior to other previously submitted and the query.
The disclosure reasonably describes the argued limitation of "wherein the data indicative of the set of filler tokens is stored in the memory prior to receiving the first query and the second query".
29. On page 12, Applicant alleges to the extent Sundaresan' s current search query and a previous search query are being equated to the first query and second query in claims 51 and 61 (for which initial and updated token sets are generated), Sundaresan' s updating, at best, only occurs for the current search query and not for the previously submitted query. It follows that Sundaresan fails to disclose "generating an updated first set of tokens based at least in part on determining which tokens of the first set of tokens generated for the first query are not associated with the set of filler tokens; generating an updated second set of tokens based at least in part on determining which tokens of the second set of tokens generated for the second query are not associated with the set of filler tokens".
Examiner disagrees because:
Diversity terms were received, updated and stored based on previously submitted prior to other previously submitted and the query.
The disclosure reasonably describes the argued limitation of "
generating an updated first set of tokens based at least in part on determining which tokens of the first set of tokens generated for the first query are not associated with the set of filler tokens;
generating an updated second set of tokens based at least in part on determining which tokens of the second set of tokens generated for the second query are not associated with the set of filler tokens".
Conclusion
30. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SyLing Yen whose telephone number is 571-270-1306.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at 571-272-4098. The fax and phone numbers for the organization where this application or proceeding is assigned is 571-273-8300.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the receptionist whose telephone number is 571-272-2100.
/SYLING YEN/Primary Examiner, Art Unit 2166
September 14, 2026
