DETAILED ACTION
The following is a FINAL office action upon examination of the application number 18/619004.
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 .
Response to Amendment
Claims 1, 3, 5-7, 9, 11, 13, 15-17, and 19 have been amended.
Claims 1-20 are pending in the application and have been examined on the merits discussed below.
Claim Objections
Claims 1 and 11 objected to because of the following informalities: these claims have been amended to recite “processing, by the processor, each of the plurality of data fragments individually based on the plurality of performance metrices”. It appears this limitation should recite “performance metrics” (see [0008] … The method may further include retrieving a plurality of query fragments related to the set of parameters and a plurality of performance metrics, from the first database). Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
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.
(Step 1) Claims 1-10 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention. Claims 11-20 are directed to system comprising a processor; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention.
(Step 2A – prong one) The claims recite an abstract idea instructing how to generate seasonality adjusted predictions, which is described by claim limitations reciting:
receiving, … a set of parameters from … a user;
querying, … a first database based on the set of parameters, wherein the first database is a synthetic calendar database that represents structured time-related information, and wherein the first database is generated using a … model;
retrieving,… from the first database, a plurality of query fragments related to the set of parameters and a plurality of performance metrices corresponding to the plurality of query fragments; and
querying, … a second database based on the plurality of query fragments:
in response to querying the second database, generating, … a plurality of data fragments;
processing, … each of the plurality of data fragments individually based on the plurality of performance metrices; and
generating, … a seasonally adjusted response based on the processed plurality of data fragments.
The identified limitations in the claims describing generating seasonality adjusted predictions (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices. Dependent claims 3, 4, 5, 6, 7, 13, 14, 15, 16, and 17, recite limitations that further describe/narrow the abstract idea (i.e., generating seasonality adjusted predictions); therefore, these claims are also found to recite an abstract idea.
(Step 2A – prong two) This judicial exception is not integrated into a practical application because additional elements such as the processor and device associated with a user in claim 1; and the processor; and memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions in claim 11, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer.
Additional elements reciting receiving, by a processor, a set of parameters from a device associated with a user…; querying, by the processor, a first database…; retrieving, by the processor, from the first database…; and querying, by the processor, a second database… do not provide an improvement to the computer or technology and only add insignificant extra-solution activities (data gathering). In the same way, additional elements in claims 9 and 19 related to input received from the device do not improve the computer or technology and only add insignificant extra-solution activities (data gathering). Additional elements such as database is generated using a machine learning (ML) model do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these limitations are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 2, 8, 10, 12, 18, and 20, related to a ML model and a Natural Language Processing (NLP) model do not yield an improvement and only generally link the abstract idea to a technological environment. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component (see Spec. [0025][0026]). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements reciting receiving, by a processor, a set of parameters from a device associated with a user do not provide an improvement to the computer or technology and only add insignificant extra-solution activities (data gathering). Additional elements in claims 9 and 19 related to input received from the device do not improve the computer or technology and only add insignificant extra-solution activities (data gathering). With respect to data gathering limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Further, the courts have recognized the storing and retrieving information in memory as well-understood, routine, and conventional, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Additional elements such as database is generated using a machine learning (ML) model do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these limitations are recited at a high level of generality and only generally link the abstract idea to a technological environment. Additional elements in claims 2, 8, 10, 12, 18, and 20, related to a ML model and a Natural Language Processing (NLP) model do not yield an improvement and only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1, 3-7, 9, 11, 13-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0012248 (Connelly); in view of US S 2023/0297550 (Kumar); in view of US 2020/0210920 (Joseph).
As per claim 1, Connelly teaches: a method for generating seasonally adjusted responses in real time, the method comprising: receiving, by a processor, a set of parameters from a device associated with a user; ([0054] As shown in FIG. 3, network device 300 includes a CPU 322 in communication with a mass memory 330 via a bus 324. Mass memory 330 may include RAM 332, a ROM 334, and other storage means. [0019] … user requesting the audience forecast to issue queries related to their own website(s) (e.g. for retargeting purposes). For example, an audience may be defined as women between the ages of 30 and 50, who are in market for European travel, and who have visited my website A but not my website B in the last 60 days. [0062] The query received at block 402 may be received as part of a request for a real time prediction of an advertising audience volume over a future time period. Such a request may, in some embodiments, be received from a user. In some embodiments, the request may be received from an administrator, operator, or other person in control of audience volume prediction server(s). In some embodiments the request may also include the future time period. [0063] … the past time period of historical data may be received from and/or specified by a user of process 400)
querying, by the processor, a first database based on the set of parameters, …([0064] At block 406, stored historical audience data may be retrieved based on the query and/or past time period. In some embodiments, retrieval of data may be made from a database or other data store, such as data storage 110 and/or data stored in mass memory of audience volume prediction server(s) 106 of FIG. 1. In some embodiments, the historical audience data retrieved may be based on the past time period of historical data determined at block 404. Moreover, in some embodiments, the historical audience data retrieved may include a plurality of historical advertising audience volumes).
retrieving, by the processor from the first database, a plurality of query fragments related to the set of parameters and a plurality of performance metrices corresponding to the plurality of query fragments; and ([0064] At block 406, stored historical audience data may be retrieved based on the query and/or past time period. In some embodiments, retrieval of data may be made from a database or other data store, such as data storage 110 [0066] …if the past time data of historical data is six months (e.g. the last six months from the current time, or a specified range of dates that is six months long) [0069] Time period for collected data …One month ago until current time … Two months ago until one month … Three months ago until two months ago … Four months ago until three months ago … Five months ago until four months ago… [0070] In this example, historical data is retrieved up until five months from the current time.)
a plurality of data fragments; processing, by the processor, each of the plurality of data fragments individually based on the plurality of performance metrices; ([0069] …the first weights may be weighting factors that determine the weights given to the various data when calculating the predicted audience volume. In some embodiments, more recent data may be weighted for heavily than older data. For example, data collected in the last month may be weighted more heavily than data collected in the previous month, and so forth… [0071] …seasonality (e.g. data collected in the winter is weighted differently than data collected in the summer), special events (e.g. weighting related to holidays, natural disasters, entertainment events, and the like) [0072] …PAV=p(1)*w(1)+p(2)*w(2)+p(3)*w(3)+ . . . p(n)*w(n) where p(i) represents the historical data being analyzed and w(i) represents one or more weight factors applied to the particular data).
generating, by the processor, a seasonally adjusted response based on the processed plurality of data fragments ([0066] …a scaling factor may be used to adjust for a known seasonality effect; e.g., such as the effect that in May people are 1.5 times more likely to interested in pool cleaning and other warm-weather-related products or services. [0072] At block 506, the predicted audience volume may be determined for the future time period based on combined weights for the stored audience data. [0071] At block 504, a further N number of weights may be determined for the stored audience data based on other factors and on a selectable scaled smoothing. …seasonality (e.g. data collected in the winter is weighted differently than data collected in the summer), special events (e.g. weighting related to holidays, natural disasters, entertainment events, and the like) [0075] After the predicted audience volume has been determined, it may be provided to the user via a report screen or other means (described in more detail with regard to FIG. 7). In some embodiments, the predicted audience volume may be provided to the user as a number of persons that are predicted to be reached by the specified query for the determined future time period, and/or a range of an estimated number of persons predicted to be reached).
Although not explicitly taught by Connelly, Kumar teaches: wherein the first database is generated using a machine learning (ML) model… ([0079] … create a business ontology with a harmonized logical data model of its data. The approach would entail mapping the logical data model to the physical data model pointing at the appropriate data sources. This would mean business users can freely explore data in a business-friendly language without data engineering effort. Takes the requirements of a data view (e.g., the data view requested by a user, such as a business user), and designs a logical data model that contains information such as schema of entities, primary-keys, foreign-keys, and cardinality between tables (one-to-many, many-to-one, many-to-many). [0081] … operable to query the logical data model [0138] … machine learning algorithms can be utilized to detect and update the models).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Connelly with the aforementioned teachings of Kumar with the motivation of providing multiple data views without altering source data (Kumar [Abstract]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Kumar to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of machine learning to build a model.
Although not explicitly taught by Connelly, Joseph teaches: wherein the first database is a synthetic calendar database that represents structured time-related information ([0035] … data 222 may be arranged in the datasets 206 according to days, e.g., Day 1, Day 2 up to Day N. The calendar data may comprise basic calendar information, including holidays. The calendar data may also comprise customized information and special days configured for particular user contexts or user preferences. [0040] … computer server 208 further comprises a table generator 226 and a table of holidays and special days 228 [0048] … historical data generated using the described techniques. [0057] … generate a table of holidays and special days).
querying, by the processor, a second database based on the plurality of query fragments; [0017] Once the most closely aligned day is determined, its associated historical data can be extracted from data storage (e.g., a database or other data storage device) [0038] …The demand forecasting engine 203 can thereafter access historical data 222 corresponding with the aligned date 232 [0045] … demand from the week before and after the holiday or special day in the previous year).
in response to querying the second database, generating, by the processor, a plurality of data fragments; ([0037] As shown in the illustrated embodiment, the input to the demand forecasting engine 203 further includes the actual historical demand data for one or more aligned date(s) in previous years [0038] …The demand forecasting engine 203 can thereafter access historical data 222 corresponding with the aligned date 232. [0042] …historical data sample associated with the aligned date can then be input into the demand forecasting engine 203 [0045] … demand from the week before and after the holiday or special day in the previous year [0058] …demand forecast for the forecast date is determined using the historical data associated with the aligned date)
Further, in addition to Connelly, Joseph also teaches: generating, by the processor, a seasonally adjusted response based on the processed plurality of data fragments ([0016] …obtain the most relevant historical data for use in improved demand forecast modeling and analysis for the forecast date [0045] …taking the average of the demand from the week before and after the holiday or special day in the previous year and using that value as the demand for the forecast date. In another embodiment, the modified demand can be adapted to shift the aligned day by plus or minus one week in the previous year and using that demand data directly. [0061] … take the average of the demand forecasts for the dates a week before and a week after Thanksgiving Day in the previous year and assign that average demand as the demand value for the forecasting operations).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Connelly with the aforementioned teachings of Joseph with the motivation of computing forecasts (Joseph [0025]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Joseph to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of a database with generated calendar data.
As per claim 3, Connelly teaches: combining, by the processor, each of the processed plurality of data fragments to generate the seasonally adjusted response. ([0066] …a scaling factor may be used to adjust for a known seasonality effect; e.g., such as the effect that in May people are 1.5 times more likely to interested in pool cleaning and other warm-weather-related products or services. [0072] At block 506, the predicted audience volume may be determined for the future time period based on combined weights for the stored audience data. [0069] …the first weights may be weighting factors that determine the weights given to the various data when calculating the predicted audience volume. In some embodiments, more recent data may be weighted for heavily than older data. For example, data collected in the last month may be weighted more heavily than data collected in the previous month, and so forth… [0071] …seasonality (e.g. data collected in the winter is weighted differently than data collected in the summer), special events (e.g. weighting related to holidays, natural disasters, entertainment events, and the like) [0072] …PAV=p(1)*w(1)+p(2)*w(2)+p(3)*w(3)+ . . . p(n)*w(n) where p(i) represents the historical data being analyzed and w(i) represents one or more weight factors applied to the particular data [0071] At block 504, a further N number of weights may be determined for the stored audience data based on other factors and on a selectable scaled smoothing. …seasonality (e.g. data collected in the winter is weighted differently than data collected in the summer), special events (e.g. weighting related to holidays, natural disasters, entertainment events, and the like) [0075] After the predicted audience volume has been determined, it may be provided to the user via a report screen or other means (described in more detail with regard to FIG. 7). In some embodiments, the predicted audience volume may be provided to the user as a number of persons that are predicted to be reached by the specified query for the determined future time period, and/or a range of an estimated number of persons predicted to be reached).
Although not explicitly taught by Connelly, Kumar teaches: combining, by the processor, each of the processed plurality of data fragments to generate the … response ([0081] Query component 214 is operable to query the logical data model (representing the logical view)…Query component can convert queries MQL (e.g., queries written in a domain friendly language) into SQL queries that can be run on top of datasets to fetch results. [0091] … Query component 214 is operable to query the logical data model (representing the logical view) [0041] Data management system 120 can execute database queries (e.g., SQL queries) on data resources, and provision data pipelines and workflows to, e.g., export reports. The data queries can be saved, shared, and modified by one or more users and results presented through one or more interfaces. [0082] … write a simple query, such as “Show me the total sales by customer for the past year,” which would be converted into a SQL query by the query component 214 )
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Connelly with the aforementioned teachings of Kumar with the motivation of converting a user request into a query to execute on databases (Kumar [0083]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Kumar to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the querying of a database to complete a user request.
As per claim 4, Connelly teaches: wherein the plurality of performance metrices comprises a plurality of trajectory percentages ([0066] … the future time period may be related to the past time period by a scale factor… the future time period may be specified as 1.5 times the past time period) and a plurality of weightage percentages ([0069] … more recent data may be weighted for heavily than older data. For example, data collected in the last month may be weighted more heavily than data collected in the previous month, and so forth, as in the following table… Time period for collected data …One month ago until current time … Two months ago until one month … Three months ago until two months ago … Four months ago until three months ago … Five months ago until four months ago…).
As per claim 5, Connelly teaches: wherein the plurality of performance metrices comprises a plurality of trajectory percentages, and wherein the processing each of the plurality of data fragments individually based on the plurality of performance metrices further comprises: for each of the plurality of data fragments, applying, by the processor, a trajectory percentage from the plurality of trajectory percentages to a corresponding data fragment, wherein the trajectory percentage is indicative of a prediction of a pattern of change in a time frame ([0066] … the future time period may be related to the past time period by a scale factor… [0069] … Time period for collected data …One month ago until current time … Two months ago until one month … Three months ago until two months ago … Four months ago until three months ago … Five months ago until four months ago… [0072] … predicted audience volume (PAV) may be calculated through a linear sum of weighted data: PAV=p(1)*w(1)+p(2)*w(2)+p(3)*w(3)+ . . . p(n)*w(n) where p(i) represents the historical data being analyzed and w(i) represents one or more weight factors applied to the particular data).
As per claim 6, Connelly teaches: wherein the plurality of performance metrices comprises a plurality of weightage percentages, and wherein the processing each of the plurality of data fragments individually based on the plurality of performance metrices further comprises: for each of the plurality of data fragments, applying, by the processor, a weightage percentage from the plurality of weightage percentages to a corresponding data fragment, wherein the weightage percentage is indicative of growth trends and significance of events ([0066] … the future time period may be related to the past time period by a scale factor… [0069] … Time period for collected data …One month ago until current time … Two months ago until one month … Three months ago until two months ago … Four months ago until three months ago … Five months ago until four months ago… [0072] … predicted audience volume (PAV) may be calculated through a linear sum of weighted data: PAV=p(1)*w(1)+p(2)*w(2)+p(3)*w(3)+ . . . p(n)*w(n) where p(i) represents the historical data being analyzed and w(i) represents one or more weight factors applied to the particular data [0066] … the future time period may be related to the past time period by a scale factor…; indicative of trends. [0071] …a further N number of weights may be determined for the stored audience data based on other factors and on a selectable scaled smoothing. Such other factors may include but are not limited to: day of the week (e.g. data collected Saturday and Sunday is weighted different than data collected on weekdays), seasonality (e.g. data collected in the winter is weighted differently than data collected in the summer), special events (e.g. weighting related to holidays, natural disasters, entertainment events, and the like), and/or geographical factors (e.g. different weights for southern U.S. vs. eastern U.S.)).
As per claim 7, Connelly teaches: wherein each query fragment comprises a time frame, and wherein each data fragment comprises a response queried from the … database for the time frame in the corresponding query fragment ([0062] …the request may also include the future time period… [0069] … Time period for collected data …One month ago until current time … Two months ago until one month … Three months ago until two months ago … Four months ago until three months ago … Five months ago until four months ago… [0072] … the predicted audience volume may be determined for the future time period based on combined weights for the stored audience data…predicted audience volume (PAV) may be calculated through a linear sum of weighted data: PAV=p(1)*w(1)+p(2)*w(2)+p(3)*w(3)+ . . . p(n)*w(n) where p(i) represents the historical data being analyzed and w(i) represents one or more weight factors applied to the particular data).
Although not explicitly taught by Connelly, Kumar teaches: wherein each data fragment comprises a response queried from the second database for the time frame in the corresponding query fragment ([0081] Query component 214 is operable to query the logical data model (representing the logical view)…Query component can convert queries MQL (e.g., queries written in a domain friendly language) into SQL queries that can be run on top of datasets to fetch results. [0082] … write a simple query, such as “Show me the total sales by customer for the past year,” which would be converted into a SQL query by the query component 214).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Connelly with the aforementioned teachings of Kumar with the motivation of converting a user request into a query to execute on databases (Kumar [0083]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Kumar to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the querying of a database to complete a user request.
As per claim 9, Connelly teaches: wherein the set of parameters comprises a timeframe, a locale, and a dimension, associated with an input received from the device associated with the user ([0026] … Employing the user-specified query, an audience volume prediction may be provided for a future time period [0062] … the request may be received from an administrator, operator, or other person in control of audience volume prediction server(s). In some embodiments the request may also include the future time period; timeframe. [0078] … market type categories may categories for a consumer's purchase of and/or interest in goods and services related to travel, finance, retail purchases, automotive purchases: and virtually any other type of good or service. At block 602, the user may edit the query to change, add or remove in-market categories; dimension input by user. [0080] … Location type categories generally include categories associated with geographic locations (e.g. continent, country, state, province, prefecture, county, city, neighborhood, address, and the like). At block 606, the user may edit the query to change, add or remove location categories; locale).
As per claim 11, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claims 13-17, these claims recite limitations substantially similar to those addressed by the rejection of claims 3-7, respectively; therefore, the same rejections apply.
As per claim 19, this claim recites limitations substantially similar to those addressed by the rejection of claim 9, above; therefore, the same rejection applies.
Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0012248 (Connelly); in view of US 2023/0297550 (Kumar); in view of US 2020/0210920 (Joseph); in view of US 2011/0261049 (Cardno); in view of US 2020/0250688 (Ohana).
As per claim 2, Connelly teaches: …global events, locale-specific events, locale-specific demographic data, and …trajectory changes, ([0023] … Demographic type categories may include categories related to virtually any demographic statistic, including but not limited to age and gender of a person. Location type categories may be related to geographical location definitions of varying scope. For example, location type categories may include “United States residents”, “west coast U.S. residents”, “California residents”, “Los Angeles County residents”, “Burbank residents”, and so forth. [0061] … The specified categories of consumer data may be of various category types, including but not limited to market categories, demographic categories, location categories, season categories, and the like. For example, the user may specify a query of “location=California AND market=SUV purchaser” to query for consumer data on purchasers of SUVs who live in California. As another example, the user may specify a query of “location=California OR Oregon AND market=video game console” to query for consumer data on purchasers of (or individuals who evinced an interest in) video game consoles who live in California or Oregon. In some embodiments, a query may include Boolean operators and/or weighted categories. For example, a user may specify a query of “market=LuxuryCars (with 80% confidence) AND gender=Male (with 90% confidence). [0066] … the future time period may be related to the past time period by a scale factor; trajectory. [0071] … special events (e.g. weighting related to holidays, natural disasters, entertainment events, and the like), and/or geographical factors (e.g. different weights for southern U.S. vs. eastern U.S.); natural disasters (global events), entertainment events (locale-specific events). [0079] At block 604, a determination is made to tune based on one or more demographic type categories. Demographic type categories generally include categories associated with virtually demographic factor, including for example age and/or gender. At block 604, the user may edit the query to change, add or remove demographic categories; demographic data).
wherein the locale-specific events comprise …and locale-specific social, cultural,… ([0071] … special events (e.g. weighting related to holidays, natural disasters, entertainment events, and the like); entertainment events (social events), holidays (cultural events)).
Although not explicitly taught by Connelly, Kumar teaches: generating the first database, by the processor, using the ML model based on a plurality of [data] ([0079] … create a business ontology with a harmonized logical data model of its data. The approach would entail mapping the logical data model to the physical data model pointing at the appropriate data sources. This would mean business users can freely explore data in a business-friendly language without data engineering effort. Takes the requirements of a data view (e.g., the data view requested by a user, such as a business user), and designs a logical data model that contains information such as schema of entities, primary-keys, foreign-keys, and cardinality between tables (one-to-many, many-to-one, many-to-many). [0081] … operable to query the logical data model [0117] Logical data model 510 may utilize the ontology to create a model of information in a domain friendly language. [0138] … machine learning algorithms can be utilized to detect and update the models).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Connelly with the aforementioned teachings of Kumar with the motivation of providing multiple data views without altering source data (Kumar [Abstract]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Kumar to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of machine learning to build a model.
Although not explicitly taught by Connelly, Joseph teaches: … a plurality of calendars, global events, locale-specific events,… and locale-specific trajectory changes…;wherein the locale-specific events comprise … locale-specific weather patterns, … ([0002] …days and weeks in previous calendar years. [0006] …holiday or special day in the previous year(s) [0044] …exact holiday date from a previous year, by date; calendars. [0040] …calendar data 224 comprises basic calendar information, including …special days—e.g., the date of the Superbowl; global events. [0025] …Demand drivers may include past sales, store traffic, seasonality, weather, nearby events; location-specific events. [0043] …demand for goods and/or services to increase or decrease significantly in the first and/or last week of a particular month. [0024] … Each dataset 106 may contain data relating to different locations; location specific trajectory changes.)
One of ordinary skill in the art would have recognized that applying the teachings of Joseph to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of different types of data.
Although not explicitly taught by Connelly, Cardno teaches: a plurality of calendars, locale-specific events, locale-specific demographic data..; locale-specific payroll event data,… and locale-specific social, cultural, and religious events ([0447] … calendars such as national (e.g. Chinese and European calendars)… personal calendars based on data obtained from customers (e.g., anniversaries, birthdays); calendars. [0445] … Holidays [0447] … national (e.g. Chinese and European calendars); locale specific events. [0015] … the distribution of customers and the demographics of the surrounding area [1093] … the demographics of clients within a region; location specific demographic data. [0445] … payday events…; payroll. [0542] … social period such as a weekend or holiday period [0445] … family events … life milestones; social. [0449] … they could be business events, cultural events [0544] … cultural events, such as, for example Labor Day or May Day etc.; cultural. [0447] … calendars such as national (e.g. Chinese and European calendars), religious (e.g., Jewish, Moslem, Christian) [0545] … religious events, such as, for example, Christmas, Easter, Passover etc.; religious).
One of ordinary skill in the art would have recognized that applying the teachings of Cardno to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of different types of data.
Although not explicitly taught by Connelly, Ohana teaches: locale-specific school calendar ([0121] … These seasonal fluctuations can be calculated using driver data related to seasonal events, such as a vacations, holidays, and school schedules [Claim 4] … holiday calendars, local school calendars).
One of ordinary skill in the art would have recognized that applying the teachings of Ohana to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the use of different types of data.
As per claim 12, this claim recites limitations substantially similar to those addressed by the rejection of claim 2, above; therefore, the same rejection applies.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0012248 (Connelly); in view of US 2023/0297550 (Kumar); in view of US 2020/0210920 (Joseph); in view of US 2025/0077517 (Popescu).
As per claim 8, Connelly teaches: receiving, by the processor, an input from the device associated with the user; pre-processing, by the processor, the input…; and extracting, by the processor, the set of parameters based on the pre-processing ([0019] … user requesting the audience forecast to issue queries related to their own website(s) (e.g. for retargeting purposes). For example, an audience may be defined as women between the ages of 30 and 50, who are in market for European travel, and who have visited my website A but not my website B in the last 60 days. [0062] The query received at block 402 may be received as part of a request for a real time prediction of an advertising audience volume over a future time period. Such a request may, in some embodiments, be received from a user. In some embodiments, the request may be received from an administrator, operator, or other person in control of audience volume prediction server(s). In some embodiments the request may also include the future time period. [0063] … the past time period of historical data may be received from and/or specified by a user of process 400 [0064] At block 406, stored historical audience data may be retrieved based on the query …).
Although not explicitly taught by Connelly, Popescu teaches: receiving, by the processor, an input from the device associated with the user; pre-processing, by the processor, the input using a Natural Language Processing (NLP) model; and extracting, by the processor, the set of parameters based on the pre-processing ([Abstract] … text-to-SQL model comprising a first artificial neural network and configured to convert first natural language text to a first structured query language query. [0015] … Text-to-SQL is a task in natural language processing (NLP) used to automatically generate SQL queries from natural language text. [0096] … The following sentence is an example of a natural language sentences… [0097] Show the most expensive product for Acme [0098] …create one or more user sentence patterns from the natural language sentence(s). In illustration, PNLG 218 can create the following user sentence pattern from the example natural language sentence: [0099] Show me QUANTITY with NAME of MANUFACTURERS being [0100] ‘Acme’ and PRODUCT_TYPE of PRODUCT being ‘hammer’ In this example, “MANUFACTUERERS” AND “PRODUCT” can be table names, “QUANTITY” and “NAME” can be a field names in the table “MANUFACTURERS,” and “PRODUCT_TYPE” can be a field name in table “PRODUCT.” [0107] … data items extracted from user queries… [0109] … By way of example, text-to-SQL model 602 can generate the following is a SQL query 840 from the user query “How many Acme products are sold each year?”: TABLE-US-00004 select : [SALES_DETAILS].[QUANTITY] : total select sum(sales_details.quantity) as SALES_DETAILS_QUANTITY, filter : [MANUFACTURERS].[NAME] : equals : ”Acme″ where manufacturers.name=‘Acme').
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Connelly with the aforementioned teachings of Popescu with the motivation of converting a natural language query into a structured query language (Popescu [Abstract]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Popescu to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for extraction of data from user queries.
As per claim 18, this claim recites limitations substantially similar to those addressed by the rejection of claim 8, above; therefore, the same rejection applies.
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0012248 (Connelly); in view of US 2023/0297550 (Kumar); in view of US 2020/0210920 (Joseph); in view of US 12124440 (Romero).
As per claim 10, although not explicitly aught by Connelly, Romero teaches: wherein in case of failure of querying the first database, the method further comprises: receiving, by the processor, feedback from the user; and training, by the processor, the ML model based on the feedback (Col 2 ln 29-33 natural language to SQL models may refer to machine learning-based processes (ML-based processes) to convert queries in natural language into SQL statements to query a given database Col 7 ln 16-26 the user may provide feedback to the tool service by selecting the “successful” radio button if the user decides that the results and/or the final SQL query was successfully provided, or by selecting the “unsuccessful” radio button if the user decides that the results and/or the final SQL query was not successfully provided. The user may then submit the results to the tool/service by activating the “submit feedback” button. The tool/service may use the feedback to update/modify one or more models that are used to convert NLQs to final SQL queries. Col 5 ln 22-23 …trained model may be further trained/updated based on feedback from a client/user).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Connelly with the aforementioned teachings of Romero with the motivation of updating a model based on user feedback (Romero Col 5 ln 22-23). Further, one of ordinary skill in the art would have recognized that applying the teachings of Romero to the system of Connelly would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the collection of user feedback.
As per claim 20, this claim recites limitations substantially similar to those addressed by the rejection of claim 10, above; therefore, the same rejection applies.
Response to Arguments
Applicant's arguments filed 5/11/2026 have been fully considered but they are not persuasive.
With respect to the rejection under 35 USC 101, Applicant argues that the claims do not recite a judicial exception.
Examiner respectfully disagrees. Examiner maintains that the limitations in the claim describing generating seasonality adjusted predictions fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices (see Spec [002] Organizations often utilize predictive analytics for making data-driven decisions and optimizing outcomes. Accurate forecasting enables organizations to adapt to changing market conditions, identify growth opportunities, and optimize their operations. To predict KPIs such as sales, revenue, growth or the like, the existing techniques examines time series data from the past to estimate the values of KPIs in the future). The courts have used the phrases "fundamental economic practices" or "fundamental economic principles" to describe concepts relating to the economy and commerce. Examiner acknowledges that the claims recite certain additional elements; these additional elements have been evaluated/considered in step 2A – prong two.
With respect to the rejection under 35 USC 101, Applicant argues that the claims are integrated into a practical application.
Examiner respectfully disagrees. Additional elements reciting receiving, by a processor, a set of parameters from a device associated with a user…; querying, by the processor, a first database…; retrieving, by the processor, from the first database…; and querying, by the processor, a second database… do not provide an improvement to the computer or technology and only add insignificant extra-solution activities (data gathering). Additional elements such as database is generated using a machine learning (ML) model do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these limitations are recited at a high level of generality and only generally link the abstract idea to a technological environment. Accordingly, these additional element 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 in Enfish described steps of configuring a computer memory in accordance with a self-referential table, and the specification identified that the claimed invention achieved other benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements; the present claims are not directed to an analogous improvement in computer technology. The present claims directed to generating seasonality adjusted predictions provide an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) which is not an improvement in technology.
With respect to the rejection under 35 USC 101, Applicant argues that the claims amount to “significantly more.”
Examiner respectfully disagrees. The claims in BASCOM were directed to a system for filtering content retrieved from an Internet computer network, comprising a local client computer and a remote ISP server that implements at least one filtering scheme and a plurality of sets of logical filtering elements. The Federal Circuit described the concept of filtering content as an abstract idea and a method of organizing human behavior, similar to concepts previously found to be abstract. The Federal Circuit found that the elements, in combination, amounted to significantly more because of the non-conventional and non-generic arrangement that provided a technical improvement in the art. Examiner finds that the present rejected claims are different than those in BASCOM where the claims where directed to the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user; in contrast, additional elements in the present invention such as querying, by the processor, a first database…; …database is generated using a machine learning (ML) model…; and querying, by the processor, a second database… do not provide an improvement and do not provide a non-conventional arrangement of additional elements analogous to the claims in BASCOM. The claims in BASCOM were found to contain more than the abstract idea of filtering content along with the requirement to perform it on the Internet or perform it on a set of genetic computer components; none of recited hardware in the present claims offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment. When taken as an ordered combination, the ordered combination of the claimed elements adds nothing that is not already present when the elements are taken individually.
Additionally, the generation of synthetic calendar data… is directed to an abstract limitation/step; reciting that this step is performed using a machine learning model only generally links the abstract idea to a technological environment or field of use and does not provide an improvement to the computer or technology.
With respect to the rejection under 35 USC 103, Applicant argues that the art of record does not disclose the claimed limitations.
Examiner respectfully disagrees. The Applicant’s arguments are directed to newly amended features; additional search has been conducted and the rejection has been updated to address said amendments. See updated Claim Rejections - 35 USC § 103 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2025/0315682 (Brak) – discloses a system that generates calendar data by a machine learning model.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625
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