DETAILED ACTION
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 Arguments
Applicant’s arguments, see Remarks pg. 9, filed 11/15/2025, with respect to the status of the claims is hereby acknowledged.
Applicant’s arguments, see Remarks pg. 9, filed 11/15/2025, with respect to the interview summary is hereby acknowledged.
Applicant’s arguments, see Remarks pg. 10, filed 6/27/2025, with respect to the obviousness rejection of claims under 35 U.S.C. 103 is hereby acknowledged. The examiner notes that the applicant’s arguments are directed to the newly amended limitations. Therefore, the examiner will set forth a new grounds of rejection with newly found prior art in order to take into consideration the newly amended limitations.
The examiner herein acknowledges the applicant’s arguments (See Remarks pg. 10-11) regarding the obviousness rejection comprising the prior art to Maggio, however, the examiner respectfully disagrees with the applicant’s arguments. In particular, applicant argues the following:
Generally, Maggio appears to describe an "invention [that] enables a ratings provider to provide television ratings based upon data collected from set-top boxes owned by consumers electing to share their viewing information with the ratings provider." See Maggio at paragraph 59. "According to the invention, these consumers can detem1ine the extent of viewing, identifying, and demographic data shared with the ratings provider." See id. The Office Action at p. 3 alleges that:
"Maggio para 65-69 teaches that the viewer interacts with the set-top box to select particular programming for viewing and the information is then transmitted to the information processing unit 116 via network 112. A person of ordinary skill in the art would have reasonably inferred that the set-top box of Maggio comprises a metering device and thus the teachings of Maggio read on the [previously] amended limitation ... "
However, Maggio teaches away from the use of metering devices-those that are not set-top boxes. See paragraphs 12-22 of Maggio which further elucidate the cited paragraph 11 as a teaching away from amended claim 1. Thus, because Maggio teaches away from the use of a "meter device [that] is not a set-top box," Maggio -- which the Office Action uses for an inference that "a set-top box of Maggio" teaches the previously amended recitation of "the panelist physically interacted with a meter device"- could not therefore also teach that the "meter device is not a set-top box," as amended claim l now recites. Thus, Maggio does not teach or suggest the aforementioned recitations of claim 1.
First, in response to the applicant’s argument that the Office Action fails to meet the obviousness standards cited by the applicant, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). More importantly, on the issue of obviousness, the Supreme Court stated that when a patent simply arranges old elements with each performing the same function it had been known to perform and yields no more than one would expect from such an arrangement, the combination is obvious. KSR International Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385 (2007) (citing Sakraida v. AG Pro, Inc., 425 U.S. 273, 96 S. Ct. 1532, 47 L. Ed. 2d 784 (1976)). The Court further reiterated that in circumstances where the combination of two pre-existing elements did no more than they would in separate, sequential operation, the patent failed under 35 U.S.C. 103. See id. at 416-417 (citing Anderson's-Black Rock, Inc. v. Pavement Salvage Co., 396 U.S. 57, 90 S. Ct. 305, 24 L. Ed. 2d 258 (1969)). The analysis of a rejection on obviousness grounds need not seek out precise teachings directed to the specific subject matter of the challenged claim, for a court can take account of the inferences and creative steps that a person of ordinary skill in the art would employ. See id. at 418. The obvious analysis cannot be confined by a formalistic conception of the words teaching, suggestion, and motivation. Id. at 419. Further, the Court stated that common sense teaches, however, that familiar items may have obvious uses beyond their primary purposes, and in many cases a person of ordinary skill will be able to fit the teachings of multiple patents together like pieces of a puzzle. See id. at 420.
Furthermore, with respect to the applicant’s argument regarding teaching way from using metering devices that are not set-top boxes, MPEP § 2145 states that “the prior art’s mere disclosure of more than one alternative does not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solution claimed…In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004).” The combination of references as discussed in the obviousness rejection of applicant’s claims, disclose more than one alternative for collecting viewership information from client devices. Whereas Appellant appears to assert that Maggio teaches away from using metering devices that are not set-top boxes, the examiner respectfully disagrees. The portions of Maggio cited by applicant (i.e., paragraphs 11-22) do state that Nielsen's method of collecting television viewing data includes providing a specialized electronic device to each sampled household, “increasing the sample size to represent a larger proportion of the public would be prohibitively expensive...Nielsen's approach also requires that household members push buttons when they are watching television. This requirement directly affects the activity being measured and reinforces the fact that viewing activity is being monitored, both of which reduce the quality of Nielsen's rating estimates...Another drawback of the Nielsen methodology for providing ratings is that Nielsen must assume that the viewing habits of the households that do not want to be, or otherwise are not, part of the sample are the same as the viewing habits of those who agree to be part of the sample. A significant portion of the population (industry estimates non-compliance at more than 50%) may not wish to share their viewing habits with Nielsen, given that they also must disclose private data and demographic data...." In contrast, Maggio also states that in contrast to Nielsen's method of providing television ratings, "[t]he television viewing data processed by the STBs is owned by the MSOs, and therefore permission to collect the data is subject to the motivation of the MSOs. (Maggio para 18). More importantly, Maggio recognizes the advantage of Nielsen's method because "[t]he MSO-STB model as described has a few deficiencies. For example, demographic data may not be directly associated with television viewing data as it is in the Nielsen method." Maggio paragraph 20 distinguishes the benefit of collecting viewership data from devices that are owned by the consumer and not the ratings provider or the MSO. A person of ordinary skill in the would have reasonably inferred that the collection of viewership data must take into account Nielsen's method of collecting viewership data from devices owned by Nielsen or the MSO in order to determine ratings and also take into account the devices that are owned by consumers.
More importantly, Maggio and Doe dovetail with the known teachings of Nielsen for collecting viewership data based on sample sizes using metering devices (see Maggio para 11-22 and Doe para 5). As is evidence from the teachings of Doe, a metering device is used in conjunction with a television set but is not necessarily the actual television set (i.e., set-top box) wherein Maggio recognizes that some devices require viewer interaction and Doe also teaches the following in paragraph [0005]:
To acquire audience demographic information, marketing entities may employ a people meter device. The people meter is typically a small device carried by an audience member (e.g., on a belt) and/or placed near a television set and/or set-top box of the household. The demographic information may include identity-based information about the current viewer, such as name, age, sex, income, etc. People meter devices are typically provided to a household based on the household member's agreement to participate in viewing habit research initiatives, thus this demographic information is readily available. However, due to cost and/or administrative constraints, providing a people meter to every audience member and/or placing a people meter in every household that also has a set-top box is typically not practical.
The examiner noted the significant teaching value of Maggio para 11 disclosing “[t]o collect television viewing data from a household that agrees to be sampled, Nielsen uses an electronic device connected to each television monitor within the sample household which detects the programs being displayed. Nielsen owns this electronic device, and thus owns the data collected by the device.” Furthermore, Maggio para 65-69 teaches that the viewer interacts with the set-top box comprising a metering device to select particular programming for viewing and the information is then transmitted to the information processing unit 116 via network 112. A person of ordinary skill in the art would have reasonably inferred that based on the teachings of Maggio, some metering devices would require the user’s interaction and thus the teachings of Maggio discuss the operation, advantages, and disadvantages of metering devices owned by different entities and whether they are part of the television set/set-top box or not. Therefore, the examiner will set forth a new grounds of rejection in order to take into consideration the newly amended limitations.
All things considered, the combination of prior art discloses more than one alternative for collecting viewership data from devices owned by Nielsen and/or the MSO in order to determine ratings and also take into account the devices that are owned by consumers. Said alternatives do not constitute a teaching away from any of these alternatives because such disclosure does not criticize, discredit, or otherwise discourage the solutions claimed when viewed as a whole because not every metering device in every household will be owned by one type of entity, Nielsen, the MSO, or the consumers.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-8, 10-15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maggio; Frank S. et al. US 20080077951 A1 (hereafter Maggio) and in further view of Duque; Juan Carlos Niebles et al. US 20150081604 A1 (hereafter Duque) and further in view of Khoo, Denis et al. US 20040193488 A1 (hereafter Khoo) and in further view of Doe; Peter Campbell US 20080300965 A1 (hereafter Doe) and in further view of Zhang; Min et al. US20080127253A1 (hereafter Zhang).
Regarding claim 1, “a method performed by a computing system at least one processor, the method comprising: calculating a first demographic constraint average and a second demographic constraint average based on (i) first panelist demographic distribution data corresponding to a first media event of a non-panelist household and (ii) second panelist demographic distribution data corresponding to a second media event of the non-panelist household” Maggio para 73, 77-teaches a first set of ratings information for the first population is calculated based upon the data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected at a continuous frequency, as opposed to a periodic frequency; para 78-79 a second set of ratings information will be calculated for the first population, based upon the data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected continuously, as well as upon data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected periodically; para 82 the ratings provider can extrapolate the demographic data included in the first set of ratings information to the second set of ratings information, and then provide television ratings 138 based on the a combination of the two sets of ratings information. The ratings 138 can include data indicating the number of consumers 108 who watched a television program as well as the demographics of those consumers 108. Maggio para 83 corresponds to non-panelist data wherein television ratings 139 for a second population are acquired. In an exemplary embodiment, the second population can include consumers 108 that do not own STBs 104; Maggio para 84 teaches television ratings 138 for the first population are extrapolated to the television ratings 139 for the second population that were acquired in step 545. In an exemplary embodiment, this step can include extrapolating demographic data corresponding with the television ratings 138 for the first population to the television ratings 139 for the second population. Those consumers 108 or households 102 in the first population that provide demographic data can be considered the "opt-in consumers." With respect to “demographic constraint average,” as claimed above, Maggio para 87, 147 teaches extrapolating television ratings 138 and associated demographic data for the first population to the television ratings 139 for the second population can be performed using Simple Extrapolation. According to the Simple Extrapolation method, the demographic data of the opt-in consumers 108 (i.e., those consumers 108 in the first population that provide demographic data) can be used to compute the relative ratings (that is, the percentage of a program's total rating that comes from each demographic category).
Regarding “based on the first demographic constraint average, and further based on a first probability that a panelist that viewed the first media event is associated with a first demographic constraint and wherein the panelist that viewed the first media event physically interacted with a meter device about the first media event and wherein the meter device is not a set-top box, determining a first likelihood score of the non-panelist household being associated with the first demographic constraint; based on the second demographic constraint average, and further based on a second probability that a panelist that viewed the first media event is associated with a second demographic constraint, determining a second likelihood score of the non-panelist household being associated with the second demographic constraint; and estimating a household characteristic of the non-panelist household based on the first likelihood score and the second likelihood score” Maggio para 91-94 teaches “…the IDM Hybrid method begins with using viewing information of the opt-in households regarding a particular channel of interest to compute 95% confidence intervals for a demographic-specific viewing probability. When the function .psi..sup.2 is minimized, these intervals can be used as constraints on the allowed values of the viewing probabilities... IBS Hybrid method includes using an IDM algorithm (such as the IDM algorithm disclosed in Conkwright) to determine probabilities of demographic groups watching a given television channel at a given time, and then correlating the behavior of individual STBs 104 with those probabilities to assign presumed demographic descriptors to the people controlling the STBs 104. Thus, the IBS Hybrid method can include a comparison between the behavior of the opt-in consumers 108 and the individual STBs 104 in the full sample (i.e., either the second population or the first and second populations combined).” The examiner notes the significant teaching value of Maggio para 11 disclosing “[t]o collect television viewing data from a household that agrees to be sampled, Nielsen uses an electronic device connected to each television monitor within the sample household which detects the programs being displayed. Nielsen owns this electronic device, and thus owns the data collected by the device.” Furthermore, Maggio para 65-69 teaches that the viewer interacts with the set-top box to select particular programming for viewing and the information is then transmitted to the information processing unit 116 via network 112.
The portions of Maggio cited by examiner (i.e., paragraphs 11-22) do state that Nielsen's method of collecting television viewing data includes providing a specialized electronic device to each sampled household, “increasing the sample size to represent a larger proportion of the public would be prohibitively expensive...Nielsen's approach also requires that household members push buttons when they are watching television. This requirement directly affects the activity being measured and reinforces the fact that viewing activity is being monitored, both of which reduce the quality of Nielsen's rating estimates...Another drawback of the Nielsen methodology for providing ratings is that Nielsen must assume that the viewing habits of the households that do not want to be, or otherwise are not, part of the sample are the same as the viewing habits of those who agree to be part of the sample. A significant portion of the population (industry estimates non-compliance at more than 50%) may not wish to share their viewing habits with Nielsen, given that they also must disclose private data and demographic data...." In contrast, Maggio also states that in contrast to Nielsen's method of providing television ratings, "[t]he television viewing data processed by the STBs is owned by the MSOs, and therefore permission to collect the data is subject to the motivation of the MSOs. (See Maggio para 18). More importantly, Maggio recognizes the advantage of Nielsen's method because "[t]he MSO-STB model as described has a few deficiencies. For example, demographic data may not be directly associated with television viewing data as it is in the Nielsen method." Maggio paragraph 20 distinguishes the benefit of collecting viewership data from devices that are owned by the consumer and not owned by the ratings provider or the MSO. A person of ordinary skill in the would have reasonably inferred that the collection of viewership data must take into account Nielsen's method of collecting viewership data from devices owned by Nielsen or the MSO in order to determine ratings and also take into account the devices that are owned by consumers.
As will be discussed below in the teachings of Doe, Maggio and Doe dovetail with the known teachings of Nielsen for collecting viewership data based on sample sizes using metering devices (see Maggio para 11-22 and Doe para 5). As is evidence from the teachings of Doe, a metering device is used in conjunction with a television set but is not necessarily the actual television set (i.e., set-top box) wherein Maggio recognizes that some devices require viewer interaction and Doe also teaches the following in paragraph [0005]:
To acquire audience demographic information, marketing entities may employ a people meter device. The people meter is typically a small device carried by an audience member (e.g., on a belt) and/or placed near a television set and/or set-top box of the household. The demographic information may include identity-based information about the current viewer, such as name, age, sex, income, etc. People meter devices are typically provided to a household based on the household member's agreement to participate in viewing habit research initiatives, thus this demographic information is readily available. However, due to cost and/or administrative constraints, providing a people meter to every audience member and/or placing a people meter in every household that also has a set-top box is typically not practical.
The prior art provides a motivation for modifying the teachings of Maggio for estimating a household characteristic of the non-panelist viewer panels in order to make content recommendations wherein Duque teaches (para 0073-76 – applications of classifier models is the prediction of the demographic attributes for users about whom little or no demographic information is already known, as may be the case for anonymous users); para 0071 – when the user classifier model 270 for a particular demographic attribute value is applied to a prior viewing period 255A of a user, it determines with what degree of probability the user has that demographic attribute value. In other embodiments, to infer continuous attributes the model 270 is trained using regression analysis, and when applied to a user viewing period 255A determines the continuous attributes 210 and with what degree of precision. (See also Maggio para 76 disclosing the advertisers 134 can use the ratings 138 to determine the potential value of airing commercials or product placements during different television programs. Exemplary methods of determining this value can take into account both the number of consumers 108 watching a television program as well as the demographics of the consumers 108 watching the program, both of which can be included as part of the television ratings 138.)
Whereas Duque does not explicitly reference the term “demographic constraint”, the term is understood in the art, as taught by Khoo, which also teaches advertisers utilize household demographic constraints to target advertisements specifically tailored to demographic constraints. See Khoo para 54-55-56 targeting advertisements to demographic constraints at particular times.
Whereas Maggio, Duque, and Khoo do not use the term “meter device,” in an analogous art, Doe discloses data from set-top boxes are associated with people meter interface devices (para 18-20, 23, 28, 30).
In an analogous art, Zhang discloses alternative embodiments wherein a people metering device would be understood by a person of ordinary skill in the art as being coupled as part of a set-top box or a separate PC (Zhang Fig. 1 and para 56-61).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maggio’s invention for predicting of the demographic attributes for users about whom little or no demographic information is already known, as may be the case for anonymous users by extrapolating television ratings and associated demographic data for the first population for opt-in panelist to the television ratings for the second population comprising non-panelists can be performed wherein the demographic data of the opt-in consumers 108 (i.e., those consumers in the first population that provide demographic data) can be used to compute the relative ratings (that is, the percentage of a program's total rating that comes from each demographic category) because Duque recognizes the need to estimate a household characteristic of the non-panelist panels in order to make content recommendations and further consider the demographic constraints of the viewers as taught by Khoo in order to utilize the predictions for targeting advertisements. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maggio, Duque, and Khoo by further incorporating known elements of Doe to couple a set-top box to a people meter for tracking a viewer’s program watching history and be able to produce statistics from both participating and non-participating households and decrease the amount of data to be analyzed Zhang teaches a people metering device would be understood by a person of ordinary skill in the art as being coupled with connectors as part of a set-top box or via wireless connection as a separate PC.
Regarding claim 2, "wherein: the first panelist demographic distribution data identifies a plurality of demographic constraints, including the first demographic constraint and the second demographic constraint, and, for each demographic constraint, a percentage of panelists associated with that demographic constraint that viewed the first media event, and the second panelist demographic distribution data identifies, for each demographic constraint of the plurality of demographic constraints, a percentage of panelists associated with that demographic constraint that viewed the second media event” is further rejected as obvious as discussed in the rejection of claim 1 wherein Maggio para 87, 147 teaches extrapolating television ratings 138 and associated demographic data for the first population to the television ratings 139 for the second population can be performed using Simple Extrapolation. According to the Simple Extrapolation method, the demographic data of the opt-in consumers 108 (i.e., those consumers 108 in the first population that provide demographic data) can be used to compute the relative ratings (that is, the percentage of a program's total rating that comes from each demographic category).
Regarding claim 3, “wherein: calculating the first demographic constraint average comprises calculating an average of the percentage of panelists associated with the first demographic constraint that viewed the first media event and the percentage of panelists associated with the first demographic constraint that viewed the second media event; and calculating the second demographic constraint average comprises calculating an average of the percentage of panelists associated with the second demographic constraint that viewed the first media event and the percentage of panelists associated with the second demographic constraint that viewed the second media event” is further rejected as obvious as discussed in the rejection of claims 1-2 wherein Maggio para 87, 147 teaches extrapolating television ratings 138 and associated demographic data for the first population to the television ratings 139 for the second population can be performed using Simple Extrapolation. According to the Simple Extrapolation method, the demographic data of the opt-in consumers 108 (i.e., those consumers 108 in the first population that provide demographic data) can be used to compute the relative ratings (that is, the percentage of a program's total rating that comes from each demographic category).
Regarding claim 4, “wherein: the first probability that a panelist that viewed the first media event is associated with the first demographic constraint and the second probability that a panelist that viewed the first media event is associated with the second demographic constraint are based on consumption data collected from panelist household, the consumption data corresponding to media events that occurred at the same time as the first media event and the second media event” is further rejected as obvious as discussed in the rejection of claims 1-3 wherein Maggio para 91-94 teaches “…the IDM Hybrid method begins with using viewing information of the opt-in households regarding a particular channel of interest to compute 95% confidence intervals for a demographic-specific viewing probability. When the function .psi..sup.2 is minimized, these intervals can be used as constraints on the allowed values of the viewing probabilities... IBS Hybrid method includes using an IDM algorithm (such as the IDM algorithm disclosed in Conkwright) to determine probabilities of demographic groups watching a given television channel at a given time, and then correlating the behavior of individual STBs 104 with those probabilities to assign presumed demographic descriptors to the people controlling the STBs 104. Thus, the IBS Hybrid method can include a comparison between the behavior of the opt-in consumers 108 and the individual STBs 104 in the full sample (i.e., either the second population or the first and second populations combined).”
Regarding claim 5, “wherein: the first probability that a panelist that viewed the first media event is associated with the first demographic constraint and the second probability that a panelist that viewed the first media event is associated with the second demographic constraint are based on consumption data collected from panelist household, the consumption data corresponding to media events that occurred irrespective of times at which the first media event and the second media event occurred and irrespective of broadcast channels to which the first media event and the second media event correspond” is further rejected as obvious as discussed in the rejection of claims 1-4 wherein Maggio para 78-80, 93-95 teaches first and second sets of ratings information can be derived from different populations of consumers 108 at different times wherein a second set of ratings information will be calculated for the first population, based upon the data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected continuously, as well as upon data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected periodically; para 82 the ratings provider can extrapolate the demographic data included in the first set of ratings information to the second set of ratings information, and then provide television ratings 138 based on the a combination of the two sets of ratings information. The ratings 138 can include data indicating the number of consumers 108 who watched a television program as well as the demographics of those consumers 108.
Regarding claim 6, “wherein: the first panelist demographic distribution data and the first media event correspond to a first time period, and the second panelist demographic distribution data and the second media event correspond to a second time period, different from the first time period” is further rejected as obvious as discussed in the rejection of claim 1 wherein Maggio para 78-80, 93-95 teaches first and second sets of ratings information can be derived from different populations of consumers 108 at different times wherein a second set of ratings information will be calculated for the first population, based upon the data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected continuously, as well as upon data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected periodically; para 82 the ratings provider can extrapolate the demographic data included in the first set of ratings information to the second set of ratings information, and then provide television ratings 138 based on the a combination of the two sets of ratings information. The ratings 138 can include data indicating the number of consumers 108 who watched a television program as well as the demographics of those consumers 108.
Regarding claim 7, “wherein: the first media event corresponds to a first broadcast channel, and the second media event corresponds to a second broadcast channel, different from the first broadcast channel” is further rejected as obvious as discussed in the rejection of claims 1-6 wherein Maggio para 78-80, 93-95 teaches first and second sets of ratings information can be derived from different populations of consumers 108 at different times wherein a second set of ratings information will be calculated for the first population, based upon the data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected continuously, as well as upon data retrieved from consumers 108 whose sharing preferences allow time/channel data 128 to be collected periodically; para 82 the ratings provider can extrapolate the demographic data included in the first set of ratings information to the second set of ratings information, and then provide television ratings 138 based on the a combination of the two sets of ratings information. The ratings 138 can include data indicating the number of consumers 108 who watched a television program as well as the demographics of those consumers 108. See also Duque teaches para 0073 – applications of classifier models is the prediction of the demographic attributes for users about whom little or no demographic information is already known, as may be the case for anonymous users; para 0051-0068 – the video classifier models 214 are applied to each of the videos viewed during the time period, resulting in a per-video vector of scores, one score for each demographic value; for one each video (first tuning event) here might be a score for Gender:Male classifier model, a Gender:Female classifier model, and a classifier for each of the number of distinct age ranges.
Regarding claim 8, “further comprising: receiving, from a set-top box of the non-panelist household, data comprising the first media event and the second media event” is further rejected as obvious as discussed in the rejection of claims 1-7 wherein Maggio para 63, 67, 72 further teaches the data collected from each consumer 108 in the first population can comprise consumer preferences regarding whether or not the consumer desires to share data with a ratings provider, the type of data the consumer wishes to share with a ratings provider, and the frequency at which the consumer wishes to share data with a ratings provider, as well as demographic data and time/channel data 128. Demographic data can correspond with members of the consumer's 108 household 102, and the time/channel data 128 can comprise data indicating the programs watched in the consumer's 108 household 102. See also Duque teaches para 0073 – applications of classifier models is the prediction of the demographic attributes for users about whom little or no demographic information is already known, as may be the case for anonymous users; para 0051-0068 – the video classifier models 214 are applied to each of the videos viewed during the time period, resulting in a per-video vector of scores, one score for each demographic value; for one each video (first tuning event) here might be a score for Gender:Male classifier model, a Gender:Female classifier model, and a classifier for each of the number of distinct age ranges. See also Doe para 15-20 data collected comprises channel changes and time of day channel information.
Regarding claim 10, “wherein the household characteristic of the non-panelist household is representative of at least one of a member of the non-panelist household, a number of members of the non-panelist household, demographics of members of the non-panelist household, or a number of televisions in the non-panelist household” is further rejected as discussed in the rejection of claims 1-8 wherein Maggio para 49 disclosing "demographic" or "demographic data" refers to characteristics of a population, sample, or individual, including but not limited to race, ethnicity, gender, age, religion, income level, educational background, profession, and geographic location. See also Maggio para 92 - extrapolating television ratings 138 and associated demographic data for the first population to the television ratings 139 for the second population can be performed using the IBS Hybrid method. The phrase "individual behavior system" or "IBS" is used to refer to audience measurement methods that rely on characterizing individual STBs 104 based on their observed behavior. See also Duque para 0073 discloses that applications of classifier models is the prediction of the demographic attributes for users about whom little or no demographic information is already known, as may be the case for anonymous users; For example, the demographics analysis module 119 might predict, for a given anonymous user, that the user's viewing history over some prior time period indicates with strong probability that the user is a female with an age between 13 and 17 years wherein the strong probability suggests a certainty threshold. Duque teaches para 0068 - for each demographic attribute value, the score bit vectors are added together, resulting in a distribution of score values for that demographic attribute value.
The non-transitory computer-readable storage medium claims 11-15 and the system claims 17-20 are grouped and rejected with the method claims 1-8 and 10 because the steps of the method are met by the disclosure of the apparatus and methods of the reference(s) as discussed above, and because the steps of the method are easily converted into steps performed by a computer program product by one skilled in the art.
Claims 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Maggio; Frank S. et al. US 20080077951 A1 (hereafter Maggio) and in further view of Duque; Juan Carlos Niebles et al. US 20150081604 A1 (hereafter Duque) and further in view of Khoo, Denis et al. US 20040193488 A1 (hereafter Khoo) and in further view of Doe; Peter Campbell US 20080300965 A1 (hereafter Doe) and in further view of Zhang; Min et al. US20080127253A1 (hereafter Zhang) and in further view of Lambert; Diane et al. US 20120260278 A1 (hereafter Lambert).
Regarding claim 9, “wherein: determining the first likelihood score comprises dividing the first demographic constraint average by the first probability, and determining the second likelihood score comprises dividing the second demographic constraint average by the second probability” the combination of Maggio, Duque, and Khoo and Zhang are silent with respect to a “dividing” feature. In an analogous art, Lambert renders the limitation obvious wherein Lambert’s invention in para 72-82 teaches generating a household model comprising for each channel, each time block for the channel, and for each demographic segment (502), determines an expected number of viewers belonging to the demographic segment for the channel at the time block. In some implementations, the process 500 determines the likelihood by determining a probability that at least one member of the household was viewing the channel at the time block (504). For example, the denominator of the final equations in section 4.3 is used to determine this probability. Lambert also teaches the following:
[0077] Each household contributes fractionally to the demographic segments of an audience. That is, a household h is represented by a vector (e.sub.h1Nt . . . e.sub.hDNt) that describes its expected number of viewers of network N in time block t in each of the demographic segments for that household. Many of the terms e.sub.d in the household vector are usually zero, due to the number of demographic segments outnumbering the number of members of a typical household.
[0078] Given a total of H households, the estimated fraction A.sub.d of the audience of N at time block t in a demographic segment d is the estimated number of viewers in demographic segment d divided by the total number of viewers of network N at channel time block t.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Maggio, Duque, Khoo, Doe and Zhang for predicting of the demographic attributes for users about whom little or no demographic information is already known, as may be the case for anonymous users by extrapolating television ratings and associated demographic data for the first population for opt-in panelist to the television ratings for the second population comprising non-panelists can be performed wherein the demographic data of the opt-in consumers 108 (i.e., those consumers in the first population that provide demographic data) can be used to compute the relative ratings (that is, the percentage of a program's total rating that comes from each demographic category) by further incorporating known elements of Lambert for generating a household model comprising for each channel, each time block for the channel, and for each demographic segment, determines an expected number of viewers belonging to the demographic segment for the channel at the time block and determines how each household contributes fractionally to the demographic segments of an audience comprising the likelihood by determining a probability that at least one member of the household was viewing the channel at the time block.
The non-transitory computer-readable storage medium claim 16 is grouped and rejected with the method claims 1-10 because the steps of the method are met by the disclosure of the apparatus and methods of the reference(s) as discussed above, and because the steps of the method are easily converted into steps performed by a computer program product by one skilled in the art.
CONCLUSION
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 ALFONSO CASTRO whose telephone number is (571)270-3950. The examiner can normally be reached on Monday to Friday from 10am to 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Flynn can be reached. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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