Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is in response to the filing with the office dated 06/10/2026.
Claims 1, 2, 7, 9, 10, 15, 17 and 18 have been amended. Claims 1, 9 and 17 are independent claims. Claims 1-20 are presented for examination.
Response to amendment/arguments
3. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 101 as the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, have been fully considered. However, Examiner respectfully disagrees with the applicant’s argument. See response to arguments section. The rejection has been maintained.
4. Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 102 (a)(i) and 103(a) but are moot in view of the new grounds of rejection, thus necessitated the new ground of rejection as 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).
Response to 101 Rejection
5. Applicant’s arguments on page 9 regarding claim 1 states “Independent claims 1, 9 and/or 17 and dependent claims 2, 7, 10, 15 and 18 are amended based upon the proposed claim amendments and/or the feedback of the Examiner and are therefore believed to overcome the rejection. Claim 1 does not recite a generalized data analysis or evaluation that could be performed mentally. Instead, claim 1 requires generating an executable time constraint determination command comprising a function in a data management system language, and executing that command to determine a time constraint and retrieve a subset of data from the data structure. As acknowledged in the Office Action, the Office's mental- process analysis relies on characterizing the claim at a high level (e.g., page 4 of the Office Action), but the claim as amended provides for concrete machine-executable operations that are performed according to a data management system language and applied to a data structure to retrieve data. These operations cannot be practically performed in the human mind or with pen and paper. Claim 1 further integrates any alleged abstract idea into a practical application by tying the language model output to execution against a data structure. The claim does not merely analyze or interpret data, but instead generates an executable command and uses that command to retrieve data from a data structure based upon a computed time constraint. This is a specific application of computing technology directed to how queries are translated into executable operations and how data structures are accessed, rather than a generalized concept of evaluating a query and providing a response. Therefore, withdrawal of the rejection is respectfully requested”.
Examiner respectfully disagrees with the applicant as, the amended claim limitations “generating, based upon one or more characteristics of a data structure, a data structure template”; “analyzing the data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint and retrieving the subset of data from the data structure” all are processes, that under broadest reasonable interpretation, covers performance of the limitation in the mind. There is nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. These limitations at the high level of generality as drafted, would encompass a user to generate/ create a data structure based on the characteristics and associate the characteristic to the time constraint received from the query and based on that, retrieve the subset of data which is mentally performable as an evaluation or judgement. If a claim limitation, 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.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of, the invention being “model” where a model is used to create an executable command is recited at a high level of generality as generic computer components. These additional elements amount to nothing more than mere instructions to apply the recited abstract idea on a computer, under MPEP 2106.05(f). The additional element “displaying said media content” the resulting information amount to mere data outputting which is insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and outputting the result of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recitation of generic computing components is still mere instructions to apply the exception under MPEP 2106.05(f) and does not provide significantly more. The “creating an executable command using a model” element that was identified as insignificant extra-solution activity as mere data outputting when re-evaluated still does not provide significantly more. Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract.
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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106; See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Pursuant to Step 1, claims 9-16 recite a machine-readable medium which are directed to the statutory category of a manufacture. Claims 17-20 recite a computing device, which are directed to a machine.
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Under the 2019 PEG, claims are deemed to be directed to an abstract idea if they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Here, claims 1, 12 and 17 are directed to an abstract idea categorized under mental processes. Courts consider a mental process if it “can be performed in the human mind, or by a human using a pen and paper.” MPEP 2016(a)(2)(III). Courts also consider a mental process as one that can be performed in the human mind and is merely using a computer as a tool to perform the concept. MPEP 2016(a)(2)(III)(C)(3). Claim 1 recites a mental process because the steps recite the actions of storing and manipulating data but is recited at a high level of generality that merely used computers as a tool to perform the processes. See MPEP 2106(a)(2)(III). For example, claim 1 recites limitations of receiving a query and generating a response based on the query. The “generating…”, “using…”,“executing the command…”, “analyzing….” , “comprising…”are recited at a high level of generality and do not place meaningful limits on the abstract idea which is a task that can be performed by a human with the use of the computer as a tool. These limitations are essentially steps of generating and manipulating data at a high level of generality, which can be performed by a person using a computer as a tool.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the recited abstract idea is integrated into a practical application. In this case, as explained above, claims 1, 9 and 17 merely recite a mental process. These limitations describe “generating…”, “using…”,“executing the command…”, “analyzing….”, “comprising…” While claims 1, 9 and 17 recite additional components in the form of “language model”, “processor-executable instructions”, “memory”, these components are recited at a high level of generality, which do not add meaningful limits on the recited abstract idea to integrate it into a practical application by providing an improvement to the functioning of a computer or technology, implementing the abstract idea with a particular machine or manufacture that is integral to the claim, effecting a transformation or reduction of a particular article to a different state or thing, nor applying the abstract idea in some meaningful way beyond linking its use to computer technology. See 2019 PEG. The additional elements “receiving…”, “generating a response” amount to mere data gathering steps which are insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and outputting the result of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Pursuant to Step 2B, claims are analyzed to determine whether they recite significantly more than the abstract idea. In other words, it is determined whether the claims provide an inventive concept. In this case, claims 1, 9 and 17 do not recite limitations that amount to significantly more than the abstract idea. The limitations are steps involving processes that can be practically performed by a human with the aid of pen and paper, or as explained above, using a computer as a tool to perform the concept. For example, a The “receiving…”, “comprising…”, “generating a response” elements that were identified as insignificant extra-solution activity as mere data gathering and outputting when re-evaluated still does not provide significantly more. Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract.
Claims 2, 10 and 18 recite “generating, based upon one or more characteristics of the data structure, a data structure template…” is a process, that under broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper and likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim. These limitations, at the high level of generality as drafted, would encompass a user to look at the query and/ or utilize a training language model generate characteristic, which is mentally performable as an evaluation or judgement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or using a pen and paper 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.
Claims 3, 4, 5, 11, 12, 13 and 19 recite “generating the response comprises: using a second language model ….” are elements that were identified as insignificant extra-solution activity as mere data gathering and outputting when re-evaluated still does not provide significantly more. Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract. Accordingly, the claim recites an abstract idea.
Claims 6, 14 and 20 recite “displaying the response via a client device.” are elements that were identified as insignificant extra-solution activity as mere data gathering and outputting when re-evaluated still does not provide significantly more. Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract. Accordingly, the claim recites an abstract idea.
Claims 7 and 15 recites, “the first language model is configured to map one or more elements of the time- sensitive query to one or more corresponding fields of the data structure defined in the data structure template” is a process, that under broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper and likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim. These limitations, at the high level of generality as drafted, would encompass a user utilize the current date, which is mentally performable as an evaluation or judgement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or using a pen and paper 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.
Claims 8 and 16 recites, “wherein: the data structure comprises a relational database” is a process, that under broadest reasonable interpretation, covers performance of the limitation in the mind. There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper and likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim. These limitations, at the high level of generality as drafted, access the data from different tables in a database. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or using a pen and paper 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.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
7. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Epstein; Mark Edward (US 20150348541 A1) in view of Cohen; Jordan Rian (US 20190103107 A1) and in further view of Belcher; Thomas (US 20180137177 A1).
Regarding independent claim 1, Epstein; Mark Edward (US 20150348541 A1) teaches, a method, comprising: receiving a time-sensitive query (Fig. 4, Paragraph [0041] discloses, receiving a query “show flights from Mass to LAX to LAX on January 26, 2015” (Examiner interprets time-sensitive query as date/ time information in the query);
using a first language model to generate an executable time constraint determination command, comprising a first executable function in a data management system language, based upon a set of information comprising the time-sensitive query and the data structure template (Fig. 4, Paragraph [0042] discloses, determining a time constraint using a model based on the query. Also see [0051]);
and generating a response to the time-sensitive query based upon the subset of data (Fig. 4 Paragraph [0048] discloses generating a response based on the user query. Also see [0077]).
Epstein et al fails to explicitly teach, generating, based upon one or more characteristics of a data structure, a data structure template; executing the executable time constraint determination command according to the data management system language to determine a time constraint associated with the time-sensitive query; analyzing the data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint and retrieving the subset of data from the data structure.
Cohen; Jordan Rian (US 20190103107 A1) teaches, executing the executable time constraint determination command according to the data management system language to determine a time constraint associated with the time-sensitive query (Paragraph [0041] discloses, determining a time constraint by searching the databases. [0054] discloses, time constraint from the user query, which includes the time sensitive query (Examiner interprets user defined time constraint as not before 2 pm, before 8 am from the time-sensitive query, from the query, such as “On any Wednesday and not before 2 pm, or Thursday June 8 before 8 am”. Also see [0044], [0045]);
and generating a response to the time-sensitive query based upon the subset of data (Paragraph [0055], [0056] discloses, combining the logical connectives with the time elements and generating a response based on the intersection of the periodic set and the connectives. Also see [0086]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Epstein et al by executing the executable time constraint determination command according to the data management system language to determine a time constraint associated with the time-sensitive query, as taught by Cohen et al (Paragraph [0041])
One of the ordinary skill in the art would have been motivated to make this modification, by understanding equivalence, an automated assistant may simplify temporal constraints, allowing constraint processing with reduced computational resources as compared to the processing of unsimplified constraints, while inference may allow the automated assistant to provide suggestions in accordance with user requests as taught by Cohen et al (Paragraph [0017]).
Epstein et al and Cohen et al fails to explicitly teach, generating, based upon one or more characteristics of a data structure, a data structure template; analyzing the data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint and retrieving the subset of data from the data structure.
Belcher; Thomas (US 20180137177 A1) teaches, generating, based upon one or more characteristics of a data structure, a data structure template (Paragraph [0175] discloses, generating one or more characteristics of a data structure such as people, surgery, drug and timespan);
analyzing the data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint and retrieving the subset of data from the data structure; and generating a response to the time-sensitive query based upon the subset of data (Fig. 1, Paragraph [0201] discloses, analyzing the data structure based upon the time constraint to identify a subset of data in retrieving the subset of data/ generating a response).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Epstein et al and Cohen et al by generating, based upon one or more characteristics of a data structure, a data structure template; analyzing the data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint and retrieving the subset of data from the data structure, as taught by Belcher; et al (Paragraphs [0175], [0201]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it may be preferred to reduce the load on a server housing or providing access to the data repository or to improve performance times or the efficiency with which the computer retrieves data from the data repository. To address this, system 700 is configured to generate a reduced number and/or reduced complexity of SQL (or other language) queries from the RLQL query 100 (according to a process described herein). This can improve performance, for example, reduce accesses to a server or database that would otherwise be more timely or costly than computer operations run locally. In this way, system 700 is configured to generate one or more SQL (or other language) queries that when executed against a data repository will together retrieve a dataset larger than requested or indicated by the RLQL query 100 as taught by Belcher; et al (Paragraph [0105]).
Regarding dependent claim 2, Epstein et al, Cohen et al and Belcher et al teach, the method of claim 1.
Belcher et al further teaches, wherein the data structure template defines one or more structural elements of the data structure, comprising at least one of one or more fields of the data structure, one or more relationships of the data structure, or organization of the data structure; and using the first language model to generate the executable time constraint determination command comprises generating the executable time constraint determination command based upon the data structure template such that the first executable function corresponds to the of the data structure structural elements of the data structure (Paragraph [0201] discloses, using language model to generate time constraint based on the data structure. The structural elements comprise at least one of one or more fields of the data structure such as inpatients, drug, surgery and time filters).
Regarding dependent claim 3, Epstein et al, Cohen et al and Belcher et al teach, the method of claim 1.
Epstein et al further teaches, wherein generating the response comprises: using a second language model to generate the response based upon: the subset of data; and the time-sensitive query (Fig. 1, Paragraphs [0041], [0042] discloses, generating a response using different language models from the query).
Cohen also further teaches, using a second language model to generate the response based upon: the subset of data; and the time-sensitive query (Paragraph [0055], [0056] discloses, combining the logical connectives with the time elements and generating a response based on the intersection of the periodic set and the connectives. Also see [0086]).
Regarding dependent claim 4, Epstein et al, Cohen et al and Belcher et al teach, the method of claim 3.
Cohen et al further teaches, wherein: the second language model is the same as the first language model (Paragraphs [0106], [0107] discloses, the second language model is the same as the first language model, by detecting the parsing the query based on the previously trained natural language processing module).
Regarding dependent claim 5, Epstein et al, Cohen et al and Belcher et al teach, the method of claim 3.
Epstein et al further teaches, wherein: the second language model is different than the first language model (Paragraph [0041], [0042] discloses that the second language model is different from first language model, which is <time> from the time category is different from the <MONTH> <DAY> <YEAR> category).
Regarding dependent claim 6, Epstein et al, Cohen et al and Belcher et al teach, the method of claim 1.
Epstein et al further teaches, comprising: displaying the response via a client device (Paragraph [0077] displaying information to the user.
Regarding dependent claim 7, Epstein et al, Cohen et al and Belcher et al teach, the method of claim 1.
Cohen et al further teaches, wherein: the first language model is configured to map one or more elements of the time- sensitive query to one or more corresponding fields of the data structure defined in the data structure (Paragraph [0068] discloses, the dates are identified based on the current date and time of the query).
Belcher et al also teaches, wherein: the first language model is configured to map one or more elements of the time- sensitive query to one or more corresponding fields of the data structure defined in the data structure template (Paragraph [0201] discloses, each filter is mapped to one or more elements of the time- sensitive query to one or more corresponding fields. For example, for the RLQL query 100, any current inpatients admitted in the last 2 weeks on drug ‘Pen’ less than 5 days after having surgery ‘Hip’ in the last 6 months, the following would apply: [0202] ‘in the last 2 weeks’ only applies to admission filter [0203] ‘less than 5 days’ applies to both drug and surgery filters [0204] ‘in the last 6 months’ only applies to surgery filter).
Regarding dependent claim 8, Epstein et al, Cohen et al and Belcher et al teach, the method of claim 1.
Epstein et al further teaches, wherein: the data structure comprises a relational database (Paragraph [0034] discloses, accessing different related tables. Examiner interprets a relational database as a type of database that stores and provides access to data points that are related to one another).
Regarding independent claim 9, Epstein; Mark Edward (US 20150348541 A1) teaches, a non-transitory machine-readable medium having stored thereon processor-executable instructions that when executed cause performance of operations (Paragraph [0073]), the operations comprising: receiving a feature-sensitive query (Fig. 4, Paragraph [0041] discloses, receiving a query “show flights from Mass to LAX to LAX on January 26, 2015” (Examiner interprets time-sensitive query as date/ time information in the query);
using a first language model to generate an executable feature constraint determination command, comprising a first executable function in a data management system language, based upon a set of information comprising the feature-sensitive query and the data structure template(Fig. 4, Paragraph [0042] discloses, determining a time constraint using a model based on the query. Also see [0051]);
and generating a response to the feature-sensitive query based upon the subset of data (Fig. 4 Paragraph [0048] discloses generating a response based on the user query. Also see [0077]).
Epstein et al fails to explicitly teach, generating, based upon one or more characteristics of a data structure, a data structure template; executing the executable feature constraint determination command according to the data management system language to determine a feature constraint associated with the feature-sensitive query; analyzing the data structure based upon the feature constraint to identify a subset of data, of the data structure, relevant to the feature constraint and retrieving the subset of data from the data structure;
Cohen; Jordan Rian (US 20190103107 A1) teaches, executing the executable feature constraint determination command according to the data management system language to determine a feature constraint associated with the feature-sensitive query (Paragraph [0041] discloses, determining a time constraint by searching the databases. [0054] discloses, time constraint from the user query, which includes the time sensitive query (Examiner interprets user defined time constraint as not before 2 pm, before 8 am from the time-sensitive query, from the query, such as “On any Wednesday and not before 2 pm, or Thursday June 8 before 8 am”. Also see [0044], [0045], [0074]);
Cohen et also teaches, and generating a response to the time-sensitive query based upon the subset of data (Paragraph [0055], [0056] discloses, combining the logical connectives with the time elements and generating a response based on the intersection of the periodic set and the connectives. Also see [0086]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Epstein et al by executing the executable time constraint determination command according to the data management system language to determine a time constraint associated with the time-sensitive query; analyzing a data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint, as taught by Cohen et al (Paragraphs [0041], [0044], [0045], [0055], [0056]).
One of the ordinary skill in the art would have been motivated to make this modification, by understanding equivalence, an automated assistant may simplify temporal constraints, allowing constraint processing with reduced computational resources as compared to the processing of unsimplified constraints, while inference may allow the automated assistant to provide suggestions in accordance with user requests as taught by Cohen et al (Paragraph [0017]).
Epstein et al and Cohen et al fails to explicitly teach, generating, based upon one or more characteristics of a data structure, a data structure template (Paragraph [0175] discloses, generating one or more characteristics of a data structure such as people, surgery, drug and timespan);
analyzing the data structure based upon the feature constraint to identify a subset of data, of the data structure, relevant to the feature constraint and retrieving the subset of data from the data structure (Fig. 1, Paragraph [0201] discloses, analyzing the data structure based upon the time constraint to identify a subset of data in retrieving the subset of data/ generating a response).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Epstein et al and Cohen et al by analyzing the data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint and retrieving the subset of data from the data structure, as taught by Belcher; et al (Paragraph [0092]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it may be preferred to reduce the load on a server housing or providing access to the data repository or to improve performance times or the efficiency with which the computer retrieves data from the data repository. To address this, system 700 is configured to generate a reduced number and/or reduced complexity of SQL (or other language) queries from the RLQL query 100 (according to a process described herein). This can improve performance, for example, reduce accesses to a server or database that would otherwise be more timely or costly than computer operations run locally. In this way, system 700 is configured to generate one or more SQL (or other language) queries that when executed against a data repository will together retrieve a dataset larger than requested or indicated by the RLQL query 100 as taught by Belcher; et al (Paragraph [0105]).
Regarding dependent claim 10, Epstein et al, Cohen et al and Belcher et al teach, the non-transitory machine-readable medium of claim 9.
Belcher et al further teaches, wherein the data structure template defines one or more structural elements of the data structure, comprising at least one of one or more fields of the data structure, one or more relationships of the data structure, or organization of the data structure; and using the first language model to generate the executable time constraint determination command comprises generating the executable time constraint determination command based upon the data structure template such that the first executable function corresponds to the of the data structure structural elements of the data structure (Paragraph [0201] discloses, using language model to generate time constraint based on the data structure. The structural elements comprise at least one of one or more fields of the data structure such as inpatients, drug, surgery and time filters).
Regarding dependent claim 11, Epstein et al, Cohen et al and Belcher et al teach, the non-transitory machine-readable medium of claim 9.
Epstein et al further teaches, wherein generating the response comprises: using a second language model to generate the response based upon: the subset of data; and the feature-sensitive query (Fig. 1, Paragraphs [0041], [0042] discloses, generating a response using different language models from the query).
Cohen also further teaches, wherein generating the response comprises: using a second language model to generate the response based upon: the subset of data; and the time-sensitive query (Paragraph [0055], [0056] discloses, combining the logical connectives with the time elements and generating a response based on the intersection of the periodic set and the connectives. Also see [0086]).
Regarding dependent claim 12, Epstein et al, Cohen et al and Belcher et al teach, the non-transitory machine-readable medium of claim 11.
Cohen et al further teaches, wherein: the second language model is the same as the first language model (Paragraphs [0106], [0107] discloses, the second language model is the same as the first language model, by detecting the parsing the query based on the previously trained natural language processing module).
Regarding dependent claim 13, Epstein et al, Cohen et al and Belcher et al teach, the non-transitory machine-readable medium of claim 11.
Epstein et al further teaches, wherein: the second language model is different than the first language model (Paragraph [0041], [0042] discloses that the second language model is different from first language model, which is <time> from the time category is different from the <MONTH> <DAY> <YEAR> category).
Regarding dependent claim 14, Epstein et al, Cohen et al and Belcher et al teach, the non-transitory machine-readable medium of claim 9.
Epstein et al further teaches, the operations comprising: displaying the response via a client device (Paragraph [0077] displaying information to the user.
Regarding dependent claim 15, Epstein et al, Cohen et al and Belcher et al teach, the non-transitory machine-readable medium of claim 9.
Cohen et al further teaches, wherein: the first language model is configured to map one or more elements of the feature-sensitive query to one or more corresponding fields of the data structure defined in the data structure template (Paragraph [0068] discloses, the dates are identified based on the current date and time of the query).
Belcher et al also teaches, wherein: the first language model is configured to map one or more elements of the time- sensitive query to one or more corresponding fields of the data structure defined in the data structure template the set of information comprises a current date (Paragraph [0201] discloses, each filter is mapped to one or more elements of the time- sensitive query to one or more corresponding fields. For example, for the RLQL query 100, any current inpatients admitted in the last 2 weeks on drug ‘Pen’ less than 5 days after having surgery ‘Hip’ in the last 6 months, the following would apply: [0202] ‘in the last 2 weeks’ only applies to admission filter [0203] ‘less than 5 days’ applies to both drug and surgery filters [0204] ‘in the last 6 months’ only applies to surgery filter).
Regarding dependent claim 16, Epstein et al, Cohen et al and Belcher et al teach, the non-transitory machine-readable medium of claim 9.
Epstein et al further teaches, wherein: the data structure comprises a relational database (Paragraph [0034] discloses, accessing different related tables. Examiner interprets a relational database as a type of database that stores and provides access to data points that are related to one another).
Regarding independent claim 17, Epstein; Mark Edward (US 20150348541 A1) teaches, a computing device comprising: a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of operations (Paragraph [0073]), the operations comprising: receiving a feature-sensitive query (Fig. 4, Paragraph [0041] discloses, receiving a query “show flights from Mass to LAX to LAX on January 26, 2015” (Examiner interprets time-sensitive query as date/ time information in the query);
using a first language model to generate an executable feature constraint determination command, comprising a first executable function in a data management system language, based upon a set of information comprising the feature-sensitive query and the data structure template (Fig. 4, Paragraph [0042] discloses, determining a time constraint using a model based on the query. Also see [0051]);
and generating a response to the time-sensitive query based upon the subset of data (Fig. 4 Paragraph [0048] discloses generating a response based on the user query. Also see [0077]).
Epstein et al fails to explicitly teach, generating, based upon one or more characteristics of a data structure, a data structure template; executing the executable feature constraint determination command according to the data management system language to determine a feature constraint associated with the feature-sensitive query; analyzing the data structure based upon the feature constraint to identify a subset of data, of the data structure, relevant to the feature constraint.
Cohen; Jordan Rian (US 20190103107 A1) teaches, executing the executable feature constraint determination command according to the data management system language to determine a feature constraint associated with the feature-sensitive query (Paragraph [0041] discloses, determining a time constraint by searching the databases. [0054] discloses, time constraint from the user query, which includes the time sensitive query (Examiner interprets user defined time constraint as not before 2 pm, before 8 am from the time-sensitive query, from the query, such as “On any Wednesday and not before 2 pm, or Thursday June 8 before 8 am”. Also see [0044], [0045], [0074]);
Cohen et al also teaches, (Paragraph [0055], [0056] discloses, combining the logical connectives with the time elements and generating a response based on the intersection of the periodic set and the connectives. Also see [0086]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Epstein et al by executing the executable time constraint determination command according to the data management system language to determine a time constraint associated with the time-sensitive query; analyzing a data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint, as taught by Cohen et al (Paragraphs [0041], [0044], [0045], [0055], [0056]).
One of the ordinary skill in the art would have been motivated to make this modification, by understanding equivalence, an automated assistant may simplify temporal constraints, allowing constraint processing with reduced computational resources as compared to the processing of unsimplified constraints, while inference may allow the automated assistant to provide suggestions in accordance with user requests as taught by Cohen et al (Paragraph [0017]).
Epstein et al and Cohen et al fails to explicitly teach, generating, based upon one or more characteristics of a data structure, a data structure template; analyzing the data structure based upon the feature constraint to identify a subset of data, of the data structure, relevant to the feature constraint.
Belcher; Thomas (US 20180137177 A1) teaches, generating, based upon one or more characteristics of a data structure, a data structure template (Paragraph [0175] discloses, generating one or more characteristics of a data structure such as people, surgery, drug and timespan);
analyzing the data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint and retrieving the subset of data from the data structure; and generating a response to the time-sensitive query based upon the subset of data (Fig. 1, Paragraph [0201] discloses, analyzing the data structure based upon the time constraint to identify a subset of data in retrieving the subset of data/ generating a response).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Epstein et al and Cohen et al by analyzing the data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint and retrieving the subset of data from the data structure, as taught by Belcher; et al (Paragraph [0092]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it may be preferred to reduce the load on a server housing or providing access to the data repository or to improve performance times or the efficiency with which the computer retrieves data from the data repository. To address this, system 700 is configured to generate a reduced number and/or reduced complexity of SQL (or other language) queries from the RLQL query 100 (according to a process described herein). This can improve performance, for example, reduce accesses to a server or database that would otherwise be more timely or costly than computer operations run locally. In this way, system 700 is configured to generate one or more SQL (or other language) queries that when executed against a data repository will together retrieve a dataset larger than requested or indicated by the RLQL query 100 as taught by Belcher; et al (Paragraph [0105]).
Regarding dependent claim 18, Epstein et al, Cohen et al and Belcher et al teach, the computing device of claim 17.
Epstein et al further teaches, the operations comprising: generating, based upon one or more characteristics of the data structure, a data structure template, wherein at least one of: the set of information comprises the data structure template; or the operations comprise training a language model using the data structure template to generate the first language model (Paragraphs [0034]-[0038] discloses, building/ generating one or more characteristics/ concepts of the data structure comprising different training data/ data structure template).
Regarding dependent claim 19, Epstein et al, Cohen et al and Belcher et al teach, the computing device of claim 17.
Epstein et al further teaches, wherein generating the response comprises: using a second language model to generate the response based upon: the subset of data; and the feature-sensitive query (Fig. 1, Paragraphs [0041], [0042] discloses, generating a response using different language models from the query).
Regarding dependent claim 20, Epstein et al, Cohen et al and Belcher et al teach, the computing device of claim 17.
Epstein et al further teaches, the operations comprising: displaying the response via a client device (Paragraph [0077] displaying information to the user.
Closest Prior Art
8. The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Cohen; Jordan Rian (US 20190103107 A1) teaches, A method includes receiving an utterance at a computerized automated assistant system, and detecting, via a date/time constraint module of the computerized automated assistant system, one or more constraints in the utterance associated with a date or time. The utterance is associated with a domain. The method further comprises generating, via the date/time constraint module, a periodic set for each of the one or more constraints associated with the date or time, and combining, via the date/time constraint module, the one or more periodic sets. The method further comprises processing, via a dialogue manager module of the computerized automated assistant system, the combined periodic sets to determine an action, and executing the action at the computerized automated assistant system.
Smyros; Athena Ann (US 20180075020 A1) teaches, For language elements that indicate or suggest time, such as adverbs, these also contain date and time information that can be used to quantify time for a single piece of text or for an entire repository. This quantification of time can then be used by many applications, such as a mobile device that needs to know when to execute a command or when an investigator is trying to piece together a chain of events from different documents (Abstract).
9. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968))).
Conclusion
Applicant’s amendments/Arguments necessitated new grounds of rejection as presented in this office action. 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 SUMAN RAJAPUTRA whose telephone number is (571) 272-4669. The examiner can normally be reached between 8:00 AM - 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi (571) 272-4078 can be reached. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S. R./
Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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