DETAILED ACTION
The following non-final Office action is in response to Applicant’s submission received on 11/25/2025.
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 .
Status of the Claims
Claims 1, 7, and 12 have been amended. Claim 6 has been canceled and claim 21 has been added. Claims 1-5 and 7-21 are pending and have been examined.
Response to Arguments
Applicants’ arguments with respect to the 35 U.S.C. 103 rejections have been fully considered.
With regard to claim 19 and the argument that Farahani and Tyagi do not teach a Random Forest Regressor ensemble, this argument is persuasive and the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made below.
With regards to the argument that Tyagi does not teach electronic vehicle chargers (amended into claim 1 from claim 5), Examiner points out that Tyagi teaches the evaluated potential new location for a retail unit can be for a gas station which is equivalent to a charging station. It is further noted that as claimed, the electric vehicle charging station has limited weight in terms of impacting the positively claimed steps or functions of the claims. Here, using claim 1 as an example, the claim is producing a trained model to predict exposure data for locations with location features about existing installation locations of panels, including population counts of adults, and exposure data for these locations. The model is used to predict content exposure as a location. The vehicle chargers, while at the location, do not appear to be hat the location feature data is about. Further, it is noted that Pagany that was brought in to teach the newly added feature of population count of adults also teaches electric vehicle chargers.
Applicant argues that claim 1 recites existing chargers, and the office action likens this to Tyagi’s potential new site or location of a retail unit, which is the opposite of what is claimed. It is noted that claim 1 is concerning predicting content exposure at a location at which no panel is installed. Tyagi is used to show potential new sites or units using analysis and forecasting. Installing a panel occurs in new claim 21. The claim does not recite installing new vehicle chargers.
Applicant argues that Tyagi teaches two age groups, both of which include non-adults, and thus Tyagi does not teach the amended limitation of the population count of adults. While Tyagi does teach the concept of using location features of age, Pagany is now included in the 35 U.S.C. 103 rejection to address the newly added feature of the population count of adults.
With regard to claim 18, it is noted that applicant did not challenge or traverse the official notice taken in the previous action so this is now taken to be admitted prior art. See MPEP 2144.03.
With regard to newly added claim 21, a new art rejection has been established below to address installing of a new panel at the particular location.
Applicants’ arguments with respect to the 35 U.S.C. 101 rejections have been fully considered, but they are not persuasive.
With regards to the argument that claim 1 recites “vehicle chargers… count of adults” which is unconventional in the art, impacts accuracy of estimation / prediction, and thus makes the claim is eligible, examiner respectfully disagrees. First, the “vehicle chargers” are a feature of the locations for which data is collected to make predictions. The vehicle chargers do not impact the data collected as claimed (which is exposure data, population count of adults in a geographic area). Vehicle chargers are not deemed to be an additional element and is part of the recited abstract idea since it is descriptive of the location for which data is collected. The count of adults is also part of the recited abstract idea. Per MPEP 2106.04, a new abstract idea is still an abstract idea: The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions. The Federal Circuit has also applied this principle, for example, when holding a concept of using advertising as an exchange or currency to be an abstract idea, despite the patentee’s arguments that the concept was "new". Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). Cf. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea").
With regards to the argument that the already installed panels for displaying video content in claims 1 and 12 should be considered an additional element, examiner respectfully disagrees. These claims positively recite producing a trained machine learning engine and causing that trained machine learning engine to predict using data (location features) about installation locations. The claim does not include the installed panels as a positively recited element of the method steps of claim 1 or positively recited element of the system in claim 12. Therefore, it is not recited as an additional element of these claims. Please note, in new claim 21 installing a new panel is considered an additional element and has been discussed in the 35 U.S.C. 101 rejection below.
Applicant argues the training and “causing…predict content exposure” as an ordered combination provides an inventive concept. Examiner respectfully disagrees for the reasons given below in the 35 U.S.C. 101 rejection. It is noted that in current claims 1 and 12, only the computer and machine learning engine are viewed to be the additional elements. It is these elements that are considered in combination, while viewing the claim as a whole, when considering if the claim recites additional elements that integrate the judicial exception into a practical application (step 2A prong 2) and when considering if the claim amounts to significantly more (whether additional elements amount to an inventive concept).
Applicant further argues that claim 1 should not be characterized as market research, mere instructions, apply it, or not going beyond generally linking. Examiner respectfully disagrees. The claim, when considered as a whole, involves prediction of content views for locations and other location features (such a population counts). This reasonably falls within certain methods of organizing human activity, including commercial interactions and marketing activities. The independent claims include the additional elements of a computer and machine learning engine to implement the abstract idea. This recites the idea of using such a model without the details of how the solution is accomplished. This, since the additional elements are recited at a high-level of generality it is reasonable to say this is mere instructions to apply the exception and apply it.
Applicant argues that newly added claim 21 installs a new panel, which is a practical application. Examiner respectfully disagrees for the reasons presented in the 35 U.S.C. 101 rejection below.
Finally, applicant argues that claims 1 and 12 increase the accuracy of internal operations of a computer through unconventional training and prediction steps, and thus improve the functioning of a computer like Enfish. Examiner respectfully disagrees. As currently claimed, the limitations that appear to impact the accuracy of the prediction and the act of training are found within the recited abstract idea, namely the marketing data collected and used to predict content views for locations. See discussion of MPEP 2106.04 above, where a new abstract idea is still an abstract idea. The claim does not include technical details of how the training occurs or limitations that realize the increase the accuracy of internal operations of a computer (beyond making predictions – which, as claimed, are currently viewed to be part of the re3cited abstract idea). Further, unlike Enfish which included claims to a self-referential table for a computer database held eligible as not directed to an abstract idea because their eligibility was self-evident based on the clear improvement to computer related technology (thus not needing a full eligibility analysis), here the claims are viewed to contain a recited abstract idea and thus the full eligibility analysis would be needed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites the limitation "the one or more statistics." There is insufficient antecedent basis for this limitation in the claim. For examination purposes, this has been interpreted as –one or more statistics-. 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 1-5 and 7-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test entails considering whether the claimed subject matter falls within the four categories of statutory subject matter (i.e., process, machine, manufacture, or composition of matter). In Applicant’s case, the claims pass Step 1.
However, for Step 2A Prong One of the subject matter eligibility test, independent claim 1 for example recites an abstract idea of predicting content views for locations (this similar applies to independent system claim 12). The limitations that describe an abstract idea are indicated in bold below:
A method comprising:
producing a trained machine learning engine by training a machine learning engine in a computer to predict exposure data for locations of existing electric vehicle chargers at which panels are not currently installed;
wherein training the machine learning engine is performed based, at least in part, on:
location features for each existing installation location of a plurality of existing installation locations at which panels for displaying video content are already installed, wherein the location features for said each existing installation location include a population count of adults in a geographic area that includes a particular location at which no panel is currently installed; and
exposure data for each existing installation location of the plurality of existing installation locations; and
causing the trained machine learning engine to predict content exposure for the particular location at which no panel is currently installed by providing, to the trained machine learning engine, location features for the particular location.
The limitations indicated above fall under the abstract idea subject matter grouping of certain methods of organizing human activity because such predictive analysis described in the claims amounts to market research which falls within the subgrouping of commercial or legal interactions which includes marketing activities. Therefore, the claim falls under the sub-grouping of commercial or legal interactions. The limitations also fall under the abstract idea subject matter grouping of mental processes. If a claim under its broadest reasonable interpretation covers performance in the mind but for the recitation of generic computer elements, then it is still in the mental processes category. The claimed steps of predicting based on certain features and data encompass performance in the mind or with aid of pen and paper. The recitation of a computing device and machine learning engine do not preclude the claim from reciting an abstract idea. For example, with the telephone unit and server in the TLI Communications decision, the court noted that even though a claim may recite concrete, tangible components, these components do not exclude the claim from the reach of the abstract-idea inquiry (See TLI Communications LLC v. AV Automotive, LLC No. 15-1372 (Fed. Cir. May 17, 2016)).
For Step 2A Prong Two of the subject matter eligibility test, the abstract idea is not integrated into a practical application. The additional elements of a computer and machine learning engine to implement the abstract idea are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computers or merely using computers as a tool to perform an abstract idea. See MPEP 2106.05(f) regarding mere instructions to implement on a computer and merely using a computer as a tool. These additional elements do not go beyond generally linking the abstract idea to a particular technological environment, i.e., execution on a computer. See MPEP 2106.05(h) regarding generally linking the use of the abstract idea to a particular technological environment or field of use. Use of the computing device and machine learning engine in the claim at such a high level of generality does not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field. As explained in the Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d at 1371-72 (Fed. Cir. 2015) decision (citing Alice, 134 S. Ct. at 2359), “[s]teps that do nothing more than spell out what it means to ‘apply it on a computer’ cannot confer patent-eligibility.” Thus, the generic computer elements do not impose any meaningful limits on practicing the abstract idea. When considering the claim as a whole and how the additional elements individually and in combination are used, the additional elements do not reflect integration of the abstract idea into a practical application.
Regarding Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components or merely using computers as a tool to perform an abstract idea and generally linking to a field of use or particular technological environment. Applicant’s originally filed specification (see Fig. 3) supports this conclusion with its disclosure of a general purpose computer to perform the abstract idea. When considering the claim as a whole and how the additional elements individually and in combination are used, the additional elements do not amount to significantly more than the abstract idea itself.
The dependent claims include the limitations of the independent claim and therefore recite the same abstract idea. Accordingly, the analysis and rationale discussed above regarding the independent claim and abstract idea also apply to the dependent claims. Also, the dependent claims further limit the abstract idea to a narrower abstract idea by further limiting features of the POIs and including consideration of other features such as census blocks (see claims 2-5 and 7-11). Such narrowing creates a narrower abstract idea but does not transform the abstract idea into patent-eligible subject matter. Additional elements recited in the dependent claims include generic processing components/functionality recited at a high-level of generality (e.g., limiting the machine learning to a neural network and random forest regression ensemble model (claims 18 and 19) which do not impose any meaningful limits to integrate the abstract idea into a practical application nor do they provide for an inventive concept. Further, installing a new panel at the particular location (claim 21) is considered mere instructions to apply the exception after determining the location at which a digital sign is predicted to have content exposure, with installing being generally claimed. See MPEP 2106.05(f). This would also be considered insignificant extasolution activity as claimed, similar to an insignificant application per MPEP 2106.05(g) – again generally installing the panel after first determining the location. See Xie et al. (Location Recommendation of Digital Signage Based on Multi-Source Information Fusion) that shows it is well known, routine, and conventional to install digital panel or signage at locations. The instant specification also generally describes installing, such as in 0015.
Applicant’s claims are not patent-eligible.
Claim Rejections - 35 USC § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 7-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Farahani US 2015/0326680 A1 (hereinafter “Farahani”) in view of Tyagi et al US 9,760,840 B1 (hereinafter “Tyagi”) in further view of Pagany et al. (Electric Charging Demand Location Model—A User – and Destination-Based Locating Approach for Electric Vehicle Charging Stations).
Regarding claim 1, Farahani teaches a method comprising:
producing a trained machine learning engine by training a machine learning engine in a computer to predict exposure data (Figure 1, 0040, 0041, 0042, 0044 – training machine learning models; 0026, 0048, claim 6 of Farahani – predict future indicators of levels of interest, activity, and occupancy at a physical location) (see Tyagi below for teaching “for locations at which panels are not currently installed”);
wherein training the machine learning engine is performed based, at least in part, on (0040, 0041, 0042, 0044 – training machine learning models):
location features for each existing installation location of a plurality of existing installation locations at which panels for displaying video content are already installed (0040, 0041, 0042, 0044 – equivalent claimed “location features” in Farahani are physical parameters from physical locations and associated synthetic variables which are used for training…see 0031, 0037, 0038 further for physical locations, physical parameters and synthetic variables; 0046, 0047 – equivalent claimed “panels” in Farahani are interactive computing device 600 that displays images or messages on a display of the device and in which such device is associated with the physical location where activity is being analyzed) (see Tyagi and Pagany below for teaching “wherein the location features for each existing location include a population count of adults in a geographic area that includes a particular location at which no panel is currently installed”); and
exposure data for each existing installation location of the plurality of existing installation locations (0040 - 0044 – heuristic-based and count-based values of initial estimates of level of activity, occupancy and interest used for training purposes where for example levels can be high, medium or low); and
causing the trained machine learning engine to predict content exposure for a particular location (see Tyagi below for teaching “at which no panel is currently installed”) by providing, to the trained machine learning engine, location features for the particular location (0026, 0048, claim 6 of Farahani – predict future indicators of levels of interest, activity, and occupancy at a physical location);
Farahani does not explicitly teach locations of existing electric vehicle chargers. Tyagi teaches the evaluated potential new location for a retail unit can be for a gas station which is equivalent to a charging station (col. 6:44-55 – gas station; col. 23:58-65 “planned retail units represent potential sites that have been selected for retail units but have not yet reached existing retail unit status. In some embodiments, the planned retail units may include potential sites for placement of a retail unit and/or target areas for placement of a retail unit that may be chosen by a user”, col. 19:40-67, col. 20:1-9 – sales forecasting; col. 3:51-67 – potential sites for placements; col. 22:20-33 - rank and select highest ranked).
It 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 to modify Farahani’s machine learning and forecasting teachings of various locations to include a retail unit such as a gas or refueling station as taught by Tyagi because as suggested by Tyagi this is one example of a type of business that wants to know where they can be present and profitable (Tyagi col. 1:19-31).
Farahani does not explicitly teach that the predictions of “exposure” (i.e., the indicators of levels of interest, activity, and occupancy at a physical location taught by Farahani) are where no “panel” (i.e., computing device with display that displays images or messages taught by Farahani) is currently installed or location features for each existing location include a population count of adults in a geographic area that includes a particular location at which no panel is currently installed.
Tyagi teaches geospatial data analysis that includes sales forecasting for evaluating a potential new site or location of a retail unit (col. 23:58-65 “planned retail units represent potential sites that have been selected for retail units but have not yet reached existing retail unit status. In some embodiments, the planned retail units may include potential sites for placement of a retail unit and/or target areas for placement of a retail unit that may be chosen by a user”, col. 19:40-67, col. 20:1-9 – sales forecasting) where sales at a particular location represent a form of exposure. Therefore, Tyagi provides the teaching that new locations, where the presence of a business is not currently established, can be included in forecasting or predicting “exposure”. Tyagi further teaches looking at population data as location features, which include age ranges of the population (col 11: 40-60).
It 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 to modify Farahani’s forecasting teachings to also include forecasting where there is nothing currently installed or present for the business and using population data such as age based on Tyagi’s teaching of evaluating potential sites for the presence of a business because as suggested by Tyagi businesses want to know where they can be present and profitable (Tyagi col. 1:19-31).
While Tyagi does disclose location features including a population count by age, Farahani and Tyagi do not explicitly disclose that the location features include a population count of adults in a geographic area.
Pagany explicitly discloses considering location features of population count of adults in a geographic area, specifically for the purpose of determining locations associated with electric vehicle chargers (page 5, Table 2, and text in sections 3.1.2 and 3.2. Statistics of user groups of drivers, starting at age 18, are considered around geographic points of interest).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the combination of Farahani and Tyagi to include population count of adults in a geographic area, specifically for the purpose of determining locations associated with electric vehicle chargers in order to most effectively evaluate and identify places with the highest demand. (Pagany abstract; Tyagi col. 1:19-31).
Regarding claim 2, Farahani in view of Tyagi teaches the elements of the method of claim 1 as shown above and Farahani further teaches further comprising: obtaining, from a particular source, data for a particular location feature for a plurality of points of interests (POIs); wherein the plurality of POIs include POIs that correspond to the plurality of existing installation locations; wherein the plurality of POIs do not include any POI that corresponds to the particular location (0023 – sensors sense multiple physical parameter data from various physical locations; 0029 – physical location, area or region; 0052 – physical parameters include distance); see Tyagi for teaching “wherein the plurality of POIs include a particular set of POIs that are within a threshold distance of the particular location; and deriving data for the particular location feature for the particular location based on data, for the particular location feature, from the particular set of POIs”. Farahani does not teach the limitation regarding POIs within a threshold distance of the particular location; however, Tyagi remedies such with the teachings of building and using sales bands around a subject retail unit, e.g., a half-mile circle around the subject retail unit (col. 29:61-67, col. 30:1-46). It 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 to modify Farahani’s forecasting teachings to also include the sales bands teachings of Tyagi because as suggested by Tyagi businesses want to know where they can be present and profitable (Tyagi col. 1:19-31).
Regarding claim 3, Farahani in view of Tyagi teaches the elements of the method of claim 2 as shown above. Farahani does not teach wherein deriving data for the particular location feature for the particular location includes aggregating data, for the particular location feature, from the particular set of POIs. However, Tyagi remedies such with the teachings of building and using data for sales bands around a subject retail unit, e.g., a half-mile circle around the subject retail unit (col. 29:61-67, col. 30:1-46 – predetermined distance from the subject retail unit… each sales band may comprise demographic information, psychographic information, zoning information, a business count, an employee count, competitor information, and a resident count. In various example embodiments, for each sales band, weighting factors are applied to the harvested data as part of the sales forecast calculation.). It 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 to modify Farahani’s forecasting teachings to also include the sales bands data teachings of Tyagi because as suggested by Tyagi businesses want to know where they can be present and profitable (Tyagi col. 1:19-31) and utilizing the technique disclosed by Tyagi achieves this.
Regarding claim 4, Farahani in view of Tyagi teaches the elements of the method of claim 3 as shown above. Farahani does not teach wherein aggregating data, for the particular location feature, from the particular set of POIs includes deriving a weighted average, wherein weight applied to the particular location feature for each POI in the particular set of POIs is based, at least in part, on distance of the POI from the particular location. However, Tyagi remedies such with the teachings of building and using data including weighting factors for sales bands around a subject retail unit, e.g., a half-mile circle around the subject retail unit (col. 29:61-67, col. 30:1-46 – predetermined distance from the subject retail unit… each sales band may comprise demographic information, psychographic information, zoning information, a business count, an employee count, competitor information, and a resident count. In various example embodiments, for each sales band, weighting factors are applied to the harvested data as part of the sales forecast calculation.; col. 30:26-67, col. 31:1-10 weighting factors). It 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 to modify Farahani’s forecasting teachings to also include the sales bands data and weighting factor teachings of Tyagi because as suggested by Tyagi businesses want to know where they can be present and profitable (Tyagi col. 1:19-31) and utilizing the technique disclosed by Tyagi achieves this.
Regarding claim 5, Farahani in view of Tyagi teaches the elements of the method of claim 1 as shown above. Farahani teaches machine learning and predictions associated with “exposure” at various location as detailed in claim 1, but does not teach wherein causing the trained machine learning engine to predict content exposure for the particular location includes causing the trained machine learning engine to predict monthly impressions that would occur if a panel were installed at an electric vehicle charging station (EVCS) of the particular location. However, Tyagi teaches the evaluated potential new location for a retail unit can be for a gas station which is equivalent to a charging station (col. 6:44-55 – gas station; col. 23:58-65 “planned retail units represent potential sites that have been selected for retail units but have not yet reached existing retail unit status. In some embodiments, the planned retail units may include potential sites for placement of a retail unit and/or target areas for placement of a retail unit that may be chosen by a user”, col. 19:40-67, col. 20:1-9 – sales forecasting; col. 3:51-67 – potential sites for placements; col. 22:20-33 - rank and select highest ranked). Also, Bigby teaches estimating monthly impressions which estimate the reach to a predicted audience volume (0115). It 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 to modify Farahani’s machine learning and forecasting teachings of various locations to include a retail unit such as a gas or refueling station as taught by Tyagi because as suggested by Tyagi this is one example of a type of business that wants to know where they can be present and profitable (Tyagi col. 1:19-31). It 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 to modify Farahani’s “exposure” teachings to include monthly impressions as taught by Bigby because as suggested by Bigby this is one known technique for evaluating a business’s audience reach.
Regarding claim 7, Farahani in view of Tyagi teaches the elements of the method of claim 6 as shown above. Farahani does not teach wherein the one or more statistics include total population for the census block group. However, Tyagi teaches utilizing location features such as census block data and census results as well as total population (col. 7:39-67, col. 8:1-10, col. 8:53-67, col. 9:1-11, col. 31:43-58 census blocks located near the subject retail unit; col. 11:27-60– total population). It 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 to modify Farahani’s forecasting teachings to also include the census teachings of Tyagi because as suggested by Tyagi businesses want to know where they can be present and profitable (Tyagi col. 1:19-31) and utilizing the technique disclosed by Tyagi including use of census data achieves this.
Regarding claim 8, Farahani in view of Tyagi teaches the elements of the method of claim 1 as shown above and Farahani further teaches further comprising: obtaining, from a particular source, data for a particular location feature for a plurality of points of interests (POIs); wherein the plurality of POIs include POIs that correspond to the plurality of existing installation locations; and wherein, for each existing installation location, the location features include one or more statistics relating to number of people that spent an amount of time at the POI, of the plurality of POIs, that corresponds to the existing installation location (0043 – obtaining count-based values of persons in the physical location; 0050 – amount of time that a person is within a range of distance…duration of time in front of at the physical location).
Regarding claim 9, Farahani in view of Tyagi teaches the elements of the method of claim 1 as shown above and Farahani further teaches further comprising: obtaining, from a particular source, data for a particular location feature for a plurality of points of interests (POIs); wherein the plurality of POIs include POIs that correspond to the plurality of existing installation locations; and wherein, for each existing installation location, the location features include a construction type associated with the POI, of the plurality of POIs, that corresponds to the existing installation location (0024, 0025 – sensors acquiring physical parameter data at physical locations; 0027 – product rack).
Regarding claim 10, Farahani in view of Tyagi teaches the elements of the method of claim 1 as shown above and Farahani further teaches further comprising: obtaining, from a particular source, data for a particular location feature for a plurality of points of interests (POIs); wherein the plurality of POIs include POIs that correspond to the plurality of existing installation locations; and wherein, for each existing installation location, the location features include one or more statistics relating to number of visits to the POI, of the plurality of POIs, that corresponds to the existing installation location (0043 – obtaining count-based values of persons in the physical location; 0050 – amount of time that a person is within a range of distance…duration of time in front of at the physical location).
Regarding claim 11, Farahani in view of Tyagi teaches the elements of the method of claim 1 as shown above. Farahani does not disclose further comprising: causing the trained machine learning engine to predict content exposure for each candidate location, of a plurality of candidate locations, at which no panel is currently installed; and selecting a candidate location, from the plurality of candidate locations, at which to install a panel based, at least in part, on the content exposure predicted for each candidate location.
However, Tyagi remedies this by teaching geospatial data analysis that includes sales forecasting for evaluating and selecting a potential new site or location of a retail unit (col. 23:58-65 “planned retail units represent potential sites that have been selected for retail units but have not yet reached existing retail unit status. In some embodiments, the planned retail units may include potential sites for placement of a retail unit and/or target areas for placement of a retail unit that may be chosen by a user”, col. 19:40-67, col. 20:1-9 – sales forecasting; col. 3:51-67 – potential sites for placements; col. 22:20-33 - rank and select highest ranked) where sales at a particular location represent a form of exposure. Therefore, Tyagi provides the teaching that new locations, where the presence of a business is not currently established, can be included in forecasting or predicting “exposure” and selection to become a new site for a retail unit. It 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 to modify Farahani’s machine learning and forecasting teachings to also include forecasting and deciding on a location where there is nothing currently installed or present for the business based on Tyagi’s teaching of evaluating potential sites for the presence of a business because as suggested by Tyagi businesses want to know where they can be present and profitable (Tyagi col. 1:19-31).
Claims 12-17 directed to a system recite limitations substantially similar to those in corresponding methods claims addressed above. Since, Farahani in view of Tyagi teach the elements of those claims and Farahani further discloses system elements in Fig. 1, the same art and rationale also apply to claims 12-15, 17.
Regarding claim 20, Farahani in view of Tyagi teaches the elements of the method of claim 11 as shown above. Farahani further discloses wherein the location features include projected effectiveness of an advertising campaign associated with the predicted content exposure, and wherein the exposure data of the plurality of existing installation locations includes sales data (0018 – level of success of product marketing and sales campaigns; 0030, 0051 – advertising campaign).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Farahani US 2015/0326680 A1 (hereinafter “Farahani”) in view of Tyagi et al US 9,760,840 B1 (hereinafter “Tyagi”) in further view of Pagany et al. (Electric Charging Demand Location Model—A User – and Destination-Based Locating Approach for Electric Vehicle Charging Stations) and further in view of Official Notice, now admitted prior art.
Regarding claim 18, Farahani in view of Tyagi and in further view of Pagany teaches the elements of the system of claim 12 as shown above. Farahani discloses a machine learning engine (0040, 0041) but does not disclose where the machine learning engine comprises a neural network. However, examiner takes Official Notice that an example machine learning engine can comprise a neural network and one of ordinary skill in the art would have found it obvious to specify the machine learning engine relied upon comprises a neural network because of the inherent qualities of using a neural network for machine learning.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Farahani US 2015/0326680 A1 (hereinafter “Farahani”) in view of Tyagi et al US 9,760,840 B1 (hereinafter “Tyagi”) in further view of Pagany et al. (Electric Charging Demand Location Model—A User – and Destination-Based Locating Approach for Electric Vehicle Charging Stations) and further in view of Ramachandran (US 2022/0108354).
Regarding claim 19, Farahani in view of Tyagi and in further view of Pagany teaches the elements of the system of claim 12 as shown above. Farahani teaches training and machine learning models (0040, 0041, 0048). Tyagi teaches regression equation may be a linear regression, a polynomial regression, or any other suitable regression equation (col. 29: 60-67). However, none of Farahani, Tyagi, and Pagany disclose that the machine learning engine comprises a Random Forest Regressor ensemble model.
Ramachandran discloses that the machine learning engine comprises a Random Forest Regressor ensemble model (0038, 0040-disclosing use of a random forest algorithm. 0009, 0043 – where new locations are identified using parameters like age, location, etc.).
It 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 to modify the combination of Farahani, Tyagi, and Pagany, and specifically Farahani’s forecasting teachings and machine learning model to include use of a Random Forest algorithm of Ramachandran to maximizing the desired outcome using such a machine learning technique.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Farahani US 2015/0326680 A1 (hereinafter “Farahani”) in view of Tyagi et al US 9,760,840 B1 (hereinafter “Tyagi”) in view of Pagany et al. (Electric Charging Demand Location Model—A User – and Destination-Based Locating Approach for Electric Vehicle Charging Stations) and in further view of Xie et al. (Location Recommendation of Digital Signage Based on Multi-Source Information Fusion).
Regarding claim 21, Farahani in view of Tyagi and in further view of Pagany teaches the elements of the method of claim 1 as shown above. Farahani discloses panels at locations (0046, 0047 – equivalent claimed “panels” in Farahani are interactive computing device 600). Farahani does not explicitly teach and Tyagi teaches identifying a new location or site (col. 23:58-65, col. 19:40-67, col. 20:1-9) and Pagany teaches calculating demand at points of interest and identifies the most appropriate sites for charging stations (see at least page 13, section 6). However, Farahani, Tyagi, and Pagany does not explicitly disclose installing a new panel at the particular location.
Xie et al. discloses installing a new panel at the particular location (See page 2, second paragraph, and page 15, second paragraph, where areas are identified for installation of digital signage and installed).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include installing a digital sign, as taught be Xie et al., in the combination of Farahani / Tyagi / Pagany and, specifically, the process of identifying a new location or site of Tyagi in order to effectively improves the science of digital signage layout or site selection, prompt effective advertising efforts, and increase profitability, as discussed by Tyagi and Xie et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mongeau (US 11,263,665) teaches choosing candidate locations and analyzing data to choose geographic areas with digital players to target, where the data includes the demographic information about age (such as 18-30).
Lee at al. (Exploring electric vehicle charging patterns: Mixed usage of charging infrastructure) includes analyzing the behavior of electric vehicle owners using demographic information as it relates to choosing charging locations.
Philipsen et al. (Fast-charging station here, please! User criteria for electric vehicle fast-charging locations) discusses a study for targeting new locations for fast-charging vehicle locations including considering variables like age and gender.
Zeniya (US 2014/0379479) discusses use of digital signage installed at locations and selection of content to play on the digital signage terminal using demographic information such as age group.
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/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625