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 .
Claim Objections
Claim(s) 9 is/are objected to because of the following informalities:
In claim 9, “within a 3-minute,” should read “within a 3-minute period”.
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.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 101 because the claimed invention us directed to an abstract idea without significantly more.
[STEP 1] The claim recites at least one step or structure. Thus, the claim is to a process, which is one of the statutory categories of invention.
[STEP2A PRONG I] Claim(s) 1-13 recite steps for collecting data on children’s presence in zones, analyzing time, frequency, duration, and sequence of participation; and identifying characteristics such as, development level, social interactions, leadership roles, and potential strengths and problems. More specifically, the claims recite establishing age-appropriate attention ranges, comparing participation and attention data against such ranges, categorizing children based on nap consistency and participation levels, evaluating developmental and behavioral criteria, identifying redesign suggestions for kindergarten zones and generating charts, maps, and reports summarizing such evaluations. The recited steps, under their broadest reasonable interpretation, establish observation, evaluation and judgment, which can be performed in the human mind or with pen and paper. Therefore, the recited steps fall within an abstract idea, specifically mental process and/or certain methods of organizing human activity (see MPEP 2106.04(a)(2)).
[STEP2A PRONG II] The judicial exception is not integrated into a practical application because the claims do not recite additional elements that are significantly more than the judicial exception or meaningfully limit the practice of the judicial exception. The additional elements are RFID tags, RFID readers, and a processor. The claims do not require any specialized machine or technology that is essential to performing the claimed method. The claimed RFID components are used only as conventional tools for collecting location and participation data, while the processor performs generic analysis and reporting operations. These elements merely perform generic data collection and processing functions used to apply the abstract idea.
[STEP2B] The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements of RFID tags, RFID readers, and processors used to perform the process are generic data collection and processing functions that are known to one of ordinary skill in the art. The recitation of these additional elements do not amount to significantly more than the judicial exception, but rather adds mere instructions to link it to conventional components. The claimed steps for collecting data to predetermined criteria, categorizing children into groups, identifying strengths or problems, and producing recommendations or redesign suggestions merely constitute mental evaluations and organizing human activity that could otherwise be performed manually by teachers, evaluators, or administrators. Therefore, claims 1-13 are not directed to eligible subject matter but are found to recite a judicial exception without significantly more.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Irvin et al., (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin.
In regards to claim(s) 1, Irvin teaches a method of collecting, analyzing, and assessing children's participation in activities within a kindergarten setting (Irvin, Abstract, p. 890; “We conducted validity and feasibility testing of a real time, indoor mapping and location system (Ubisense, Inc.) within a preschool classroom… (a) determining the activity areas where children are spending the most and least time per day (e.g., music)”), comprising steps of:
a) dividing a kindergarten into a plurality of zones, wherein each zone has an RFID reader, wherein each of the plurality of zones representing a designated area focusing on distinct aspects of early childhood education and development (Irvin, p. 894; “RTLS coordinates in four activity areas of the preschool designated Art, Circle/large Blocks, Pre-academic, and Pretend play. ”) and (Irvin, Fig. 1, p. 893; Fig. 1 discloses sensors placed at zone perimeters to detect the wearable tag on a child/ren within each activity area of a classroom);
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b) assigning a set of activities to each zone, wherein a set of activities assigned to each of the plurality of zones is aligned with the corresponding aspect of early childhood education and development addressed within said zone (Irvin, Table 3, p. 896; Table 3 identifies distinct activity areas aligned with aspects of early childhood education; e.g., Music, Art, Puzzles, etc.);
c) obtaining data on each child's entrance and exit times for each zone from a data collector (Irvin, p. 891; “Our proposed alternative uses automated RTLS to map indoor spatial and temporal coordinates of both young children in the classroom.”), wherein each child has an RFID tag (Irvin, p.890; “using a Ubisense transponder tag attached to
the participating child’s shirt.”);
d) generating zone attendance data by analyzing data obtained from the data collector, wherein zone attendance data indicates the time, frequency, and duration of each child's presence within each zone (Irvin, Fig. 4, p. 897; figure 4 presents a density of each child’s location estimates per zone);
e) generating participation data for each child using zone attendance data, wherein participation data for each child indicates the time, frequency, duration, and sequence of the respective child's participation in the set of activities assigned to each zone, and wherein children's presence within each zone is interpreted as their participation in the set of activities assigned to the respective zone (Irvin, p. 895; “For participants, the location estimates were categorized by the x, y coordinates of each classroom activity area via SPSS in order to determine the duration each participant spent in each classroom activity area.”);
f) submitting participation data to a processor configured to assess children based on data indicative of their participation in the set of activities assigned to each of the plurality of zones (Irvin, p. 893; “The networked sensors… relay data to a networked laptop or personal computer running the Ubisense Location Engine, which creates a digital map that monitors tag movements within the local environment.”);
g) identifying through the processor a plurality of factors related to children's characteristics comprising to their development level, interests and preferences, time of peak engagement, total duration of attention, napping patterns, social behaviors and interactions, potential strengths and problems in children's characteristics (Irvin, p. 893; “the numerical and visual data displays represent the kind of useful information that could be provided to teachers… to quickly convey where children are spending their time in preschool, and whether or not it meets goals and expectations”),
h) generating reports on the plurality of factors related to children's characteristics and participation data (Irvin, p. 899; “RTLS automated collection, processing, and reporting of child movement data promises to allow teachers… to make more precise data-based decisions about classroom arrangements, identification of disabilities, and how best to support adult-child and child-peer interactions within activity areas.”).
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 (i.e., changing from AIA to pre-AIA ) 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Irvin et al., (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin in view of Li (“Classroom Organization: Understanding the Context in which Children are Expected to Learn”), hereinafter Li.
In regards to claim(s) 2, Irvin discloses different activity areas in a preschool (Irvin, p. 894; “Art, Circle/large Blocks, Pre-academic, and Pretend play.”) but does not explicitly disclose wherein a rotation schedule is defined for the set of activities within each zone comprising steps of: a) defining a plurality of main domains, wherein each main domain further comprises a plurality of subdomains and each subdomain further comprises a set of activities, and wherein the plurality of main domains cover a broad area of early childhood education and development and the plurality of subdomains break down each main domain into more specific areas, and the set of activities within each of the plurality of the subdomains address specific learning objectives of the corresponding subdomain.
However, Li discloses structured daily timetables in kindergartens with multiple sessions aligned to curriculum areas (Li, p. 38; “There were a number of sessions on the timetable…. Most learning activities were structured. An orderly environment, with planned routines, was observed.”). Li further discloses that kindergarten classrooms have a timetable/daily schedule and presents the amount of time allocated to different activities (Li, p. 41; Fig. 2 and Fig. 3) thereby teaching c) selecting a subdomain from the plurality of subdomains associated with a corresponding main domain for a designated time within the zone (Li, p. 41; Fig. 2 and Fig. 3); d) providing the set of activities associated with the currently selected subdomain for children within the corresponding zone during the designated time, and e) repeating steps c and d after the designated time has elapsed (Li, p. 41; Fig. 2 and Fig. 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date to implement a rotation schedule, as taught by Li, within Irvin’s zone-based RFID tracking system to automatically monitor participation across various educational activities (e.g., main domains and subdomains) during designated time periods. One skilled in the art would recognize that a structured rotation schedule as disclosed by Li would be an obvious design choice to implement into Irvin’s zone-based tracking system as rotation schedules are well known in early childhood learning environments.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Irvin et al., (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin in view of Neitzel et al., (“Children’s Early Interest-Based Activities in the Home and Subsequent Information Contributions and Pursuits in Kindergarten”) hereinafter Neitzel, and further in view of Dosman et al., (“Evidence-based milestone ages as a framework for developmental surveillance”)hereinafter Dosman.
In regards to claim(s) 3, Irvin discloses wherein identifying children's development level, comprising the steps of: a) obtaining a plurality of developmental criteria (Irvin, p. 890; “Performing ongoing data collection is valuable in that it allows teachers to monitor children’s progress and initiate specific interventions when children are not making developmental improvements, thereby ensuring kindergarten preparedness”);
b) determining the impact that each set of activities has on each criterion (Irvin, p. 891; “The ability to move among specific classroom activity areas and spend time in these activity areas is believed to selectively address core difficulties more so than others (i.e., support individualization) … and classroom location data can assist teachers and researchers in designing optimal learning environments… For instance, it may be more important for a child with ASD to spend more time in activity areas that promote language and social engagement (e.g., more time in pretend play with peers)”);
Irvin does not disclose assigning impact values to the set of activities based on their influence on development criteria, computing weighted participation values, or obtaining a participation threshold. However, Neitzel discloses assigning a measurable weight to each set of activities reflecting how much that activity shaped or predicted the relevant development or interest criteria.
c) assigning a plurality of impact values to each set of activities, wherein the impact value represents the influence of the respective set of activities on the criterion (Neitzel, p. 786; “we performed complex contrasts using the Scheffe´ multiple comparison procedure to determine if inimitable domains of activity differentiated each group from all other interest groups (Table 1).”);
d) establishing a plurality of participation values, wherein each of the plurality of the participation values determines each child's participation in each criterion, and wherein a participation value for a child in a criterion is established by multiplying the impact value that subdomains have on the respective criterion by the time the child spends in each subdomain relevant to that impact value and by summing the results of the multiplications (Neitzel, p. 787; “Frequencies, ranges, means, and standard deviations were calculated for each of the child characteristics variables; and analyses were conducted to assess gender, cognitive aptitude, and temperament differences across the four interest groups.”);
e) obtaining a participation value requirement for each child in each criterion (Neitzel, p. 786; “Children were assigned to one of four interest groups on the basis of evaluation of the defining characteristics of the activity types and combination of types prominent in their personal activity profiles”). Neitzel discloses that children’s activity profiles are evaluated against the defining characteristics of each interest group category and assigned accordingly, which under the broadest reasonable interpretation reads on obtaining a participation value requirement for each criterion, as the defining characteristics of each interest group constitute the standard against which each child’s participation profile is measured.
It would have been obvious to one of ordinary skill, in the art as of the effective filing date, to combine Irvin’s classroom activity zone tracking system with Neitzel’s weighted developmental activity analysis to improve individualized developmental assessment. Irvin discloses tracking the amount of time children spend in specific classroom activity zones and acknowledges that different activity areas support different developmental outcomes. Neitzel discloses assigning a weight to activities for evaluating child developmental profiles. One of ordinary skill in the art would recognize that applying Neitzel’s weighting method to Irvin’s per-child activity participation data would generate weighted developmental participation values that reflect the developmental influence of different activities.
Irvin does not explicitly disclose identifying children’s development level by categorizing children’s developmental status into underdeveloped, developed, or overdeveloped stages based on comparison of participation values with developmental threshold requirements.
However, Dosman discloses a developmental milestone framework in which a child’s accomplishment in each developmental domain is evaluated against age-based milestone thresholds representing expected developmental requirements thereby teaching f) categorizing individual children's development based on a comparison of the child's participation value (step d) and the participation value requirement (step e) in each criterion, into three stages comprising:
f1) underdeveloped criterion, wherein the term implies that the participation value of the child in the respective criterion is lower than its participation value requirement (Dosman, p. 561; “Our clinically relevant ‘red flags’ milestone chart uses the uppermost published age limits for items… so that a missed milestone will usually be clearly delayed and require further action.”);
f2) developed criterion, wherein the term implies that the participation value of the child in the respective criterion is equal to its participation value requirement (Dosman, p. 567; “A comprehensive milestone chart with evidence-based ages can be of tremendous value in surveillance… teaching residents how to quickly identify typical versus atypical development (90th percentile)”), and
f3) overdeveloped criterion, wherein the term implies that the participation value of the child in the respective criterion is higher than its participation value requirement (Dosman, p. 564; Table 2 identifies children whose developmental performance exceeds the expected norm e.g., “strengths in multiple areas”).
It would have been obvious to one of ordinary skill, in the art as of the effective filing date, to incorporate Dosman’s age based developmental milestone evaluation framework into the combined Irvin’s system to categorize children’s developmental status relative to expected developmental threshold. Dosman discloses comparing observed child development against age-appropriate milestone expectations to identify delayed and typical development. One of ordinary skill in the art would recognize that applying Dosman’s categorization framework to the participation values generated from the combined teaching of Irvin and Neitzel would allow for developmental assessment and identification of developmental strengths and/or problems.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Irvin et al., (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin in view of Li (“Classroom Organization: Understanding the Context in which Children are Expected to Learn”), hereinafter Li, further in view of Neitzel et al., (“Children’s Early Interest-Based Activities in the Home and Subsequent Information Contributions and Pursuits in Kindergarten”) hereinafter Neitzel, and further in view of Dosman et al., (“Evidence-based milestone ages as a framework for developmental surveillance”)hereinafter Dosman.
In regards to claim(s) 4, Irvin discloses tracking time in each activity area per child (Irvin, p. 896; “Table 3 Distribution of time spent in activity areas by two children on different days”), but does not explicitly teach a) identifying each child's interests and preferences within the plurality of zones and subdomains. However, Neitzel discloses that children’s interests are identified by tracking relative frequency and time spent in different activity types (Neitzel, p. 783; “children’s interests can be identified by examining their time spent and level of engagement in particular activities relative to others. The activities in which young children consistently involve themselves are believed to reflect general orientations or socialized preferences for particular features of engagement”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Irvin’s zone-based RFID tracking with Neitzel’s interest identification methods. Irvin teaches tracking children’s participation duration and frequency across classroom activities. Neitzel explicitly teaches the study of participation data in early childhood learning environments. One of ordinary skill in the art would have been motivated to apply Neitzel’s interest classification framework to Irvin’s tracking system to allow for classification of children’s interests within zones.
Irvin does not explicitly disclose obtaining participation time based on a mandatory participation time. However, Li discloses a1) obtaining a mandatory participation time for children wherein the mandatory participation time refers to the time that children are directed to participate in activities by their teacher or other caregivers, and wherein the mandatory participation time is obtained through a user interface by the kindergarten teacher or other caregivers (Li, p. 40; “Teachers in general had a detailed lesson plan with aims, activities and time allocation for different sessions.”); a2) generating participation time excluding mandatory participation time (Li, p. 40; “In most of the classes (seven out of nine cases), an entitlement to free choice time or free play depended on children’s efficiency in completing the class assignment. Children could play with toys or go to designated corners when they finished their work.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Irvin’s zone tracking system with Li’s mandatory and voluntary participation time to establish participation time excluding mandatory participation time. Both Irvin and Li operate in early childhood classroom settings where structured teacher directed time and voluntary free choice time co-exist. Li expressly discloses the distinction of mandatory and free choice within a classroom setting. A person of ordinary skill would recognize that implementing Irvin’s tracking system in Li’s schedule specific classroom environment would recognize that subtracting mandatory time from total zone time will provide the voluntary, or free choice participation time.
Irvin does not disclose a3) determining zones and subdomains with the highest participation time excluding mandatory participation time for each child, as their interests and preferences. However, Neitzel discloses that children’s interests are identified by their time spent in activities relative to others (Neitzel, p. 783; “children’s interests can be identified by examining their time spent and level of engagement in particular activities relative to others.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to identify zones and subdomains with the highest non-mandatory participation time as indicators of children’s interests and preferences. Neitzel discloses that children’s interests are reflected by the activities in which they voluntarily spend the most amount of time. One of ordinary skill in the art would recognize that applying Neitzel’s interest identification framework to Irvin’s tracked participation data would result in the identification of preferred activity zones.
Irvin does not explicitly disclose b) identifying each child's interests and preferences within a plurality of interest criteria. However, Neitzel discloses classifying children into interest groups based on participation profile across activity types (Neitzel, p. 786; “Children were assigned to one of four interest groups on the basis of evaluation of the defining characteristics of the activity types and combination of types prominent in their personal activity profiles”) and (p. 786, Table 1).
b1) obtaining a plurality of interest criteria wherein the interest criteria encapsulate a broad spectrum of children's interests (Neitzel, p. 786; Table 1 presents four interest groups, conceptual, procedural, creative, and socially orientated);
b2) determining the impact that each subdomain has on each criterion (Neitzel, p. 786; “we performed complex contrasts using the Scheffe´ multiple comparison procedure to determine if inimitable domains of activity differentiated each group from all other interest groups (Table 1)”);
b3) assigning a plurality of impact values to each subdomain, wherein the impact value represents the influence of the respective subdomain on the criterion (Neitzel, p. 786, Table 1). Neitzel, Table 1 represents the average proportion of time children in each interest group spent in each activity domain. Under the broadest reasonable interpretation these domain specific proportions represent the relative influence of each subdomain on each interest criterion, thereby teaches assigning an impact value to each subdomain reflecting its influence on the respective criterion.
b4) establishing a plurality of participation values, wherein each of the plurality of the participation values determines each child's participation in each interest criterion, and wherein a participation value for a child in an interest criterion is established by multiplying the impact value that subdomains have on the respective criterion by the time the child spends in each subdomain relevant to that impact value and by summing the results of the multiplications (Neitzel, p. 787; “Frequencies, ranges, means, and standard deviations were calculated for each of the child characteristics variables; and analyses were conducted to assess gender, cognitive aptitude, and temperament differences across the four interest groups.”). Under the broadest reasonable interpretation, Neitzel’s method of computing each child’s participation profile across activity domains teaches establishing a participation value for each child in each interest criterion by aggregating domain level participation data weighted by each subdomain’s relative influence on that criterion.
b5) determining interest criterion with the highest participation value for each child, as their interests and preferences within the plurality of interest criteria (Neitzel, p. 786; “The percentage of topic-centered activities reported was significantly higher for children in the conceptual group than for all the other children”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Irvin’s zone tracking system with Neitzel’s interest identification methods to produce an automated interest assessment method to identify a child’s interest based on the different activity area within a learning environment. Neitzel identifies children’s interest criteria through manual observation of activity time across domains. Irvin utilizes the collection of activity time data using RFID tracking in similar early childhood settings. A person of ordinary skill in the art would recognize that applying Neitzel’s interest classification method to Irvin’s zone-based participation tracking data would allow an automated interest assessment process.
In regards to claim(s) 5, Irvin discloses a system that continuously records time-stamped location data for each child and illustrating the density and diversity of locations frequented (Irvin, p. 897; Fig. 4), wherein identifying children's time of peak engagement, comprising the steps of:
a) dividing the time spent by children in kindergarten into smaller segments, comprising morning, noon, afternoon, and periods before or after nap (Irvin, Abstract, p.890; “Real-time indoor mapping has several implications with respect to efficiently and conveniently: (a) determining the activity areas where children are spending the most and least time per day”)
b) obtaining the frequency and duration of each child's participation in each zone during the time segments (Irvin, p. 896; Table 3), and
c) identifying the time segment with the least frequency of zone changes and the longest duration of participation for each child, as their time of peak engagement (Irvin, p. 896; Table 3 and p. 897; Fig. 4). One of ordinary skill in the art would recognize that the correlation of the distribution of time spent in table 3 with the corresponding transition heat map depicted in fig. 4 would identify intervals of concentrated engagement for each child.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Irvin et al, (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin in view of Mahone et al (“Assessment of Attention in Preschoolers”), hereinafter Mahone.
In regards to claim(s) 6, Irvin discloses the collection of the participation duration and entry/exit count data necessary to determine attention duration metrics for each child.
a) identifying each child's total duration of attention (Irvin, p. 896; Table 3; “Distribution of time spent in activity areas by two children on different days”), comprising the steps of:
a1) analyzing each child's participation data (Irvin, p. 891; “alternative uses automated RTLS to map indoor spatial and temporal coordinates of both young children in the classroom.”);
a2) computing the number of times each child enters each zone and the duration they spent at each entrance (Irvin, p.896; Table 3 presents the distribution of time spent in activity areas by two children on different days). Irvin documents per-child time in each activity zone, the RTLS system records every zone entry event, from which both entry count and time spent are computable through the existing system as disclosed by Irvin.
a3) calculating the duration of attention for each child in each zone by dividing the total duration of the child's presence in that zone by the number of times they enter the respective zone (Irvin, p. 896; Table 3 presents the total zone duration of each child) and (Irvin, p. 896; “Scanning rate was set to 1 Hz to increase the manageability of the large amount of data produced by the RTLS. The location estimates per second (M= 1.35) were averaged in order calculate time within activity areas.”). Irvin’s RTLS system generates the two inputs needed for the claimed division.
a4) identify the total duration of attention for each child by dividing the duration of attention for each child in each zone by the total number of zones (Irvin, p. 896; “Thomas in contrast to Karen spent a much greater amount of time in Pretend Play (22 min more) followed by Circle/Large Blocks (8 min more). Karen spent more time than Thomas in Art (19 min more) and Pre-Academic (3 min more).”);
b) identifying each child's total duration of attention during the child's time of peak engagement (Irvin, p. 896; Table 3 presents the distribution of time spent in activity areas), comprising the steps of:
b1) analyzing each child's participation data during their time of peak engagement (Irvin, p. 896; Table 3 presents peak engagement time of two children in different activity areas);
b2) computing the number of times each child enters each zone and the duration they spent at each entrance during their time of peak engagement (Irvin, p.896; Table 3 presents the distribution of time spent in activity areas);
b4) identify the total duration of attention for each child during their time of peak engagement by dividing the duration of attention for each child in each zone during their time of peak engagement by the total number of zones during their time of peak engagement (Irvin, p. 896; “Thomas in contrast to Karen spent a much greater amount of time in Pretend Play (22 min more) followed by Circle/Large Blocks (8 min more). Karen spent more time than Thomas in Art (19 min more) and Pre-Academic (3 min more).”).
Irvin does not explicitly disclose b3) calculating the duration of attention for each child in each zone during their time of peak engagement by dividing the total duration of the child's presence in that zone by the number of times they enter the respective zone during their time of peak engagement. However, Mahone discloses that sustained attention duration in preschool children is meaningful (Mahone, p. 4; “Attention encompasses several important neuropsychological processes that develop rapidly during the preschool years, including the ability to focus on and attend to stimuli over a period of time”) and that sustained attention can be quantified using structured, time-based assessments (Mahone, p. 14; discloses a structured computerized task “PVT” that record responses over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date to apply a time-based metric to Irvin’s RTLS system to quantify sustained engagement within a zone by relating total duration of presence to the frequency of zone re-entry during periods of observed engagement.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Irvin et al, (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin in view of Staton et al., (“Mandatory Nap Times and Group Napping Patterns in Child Care: An Observational Study”) hereinafter Staton.
In regards to claim(s) 7, Irvin discloses a system tracking children’s presence across all designated activity areas but does not explicitly teach identifying each child's napping patterns. However, Staton discloses direct observational study of napping patterns in childcare settings.
a) assigning a zone for the nap (Staton, p. 9; “This study provides new data on the association of mandatory nap time practices and napping patterns within child care rooms”);
b) obtaining time and durations of each child's naps from their participation data (Staton, p. 10; “nap times, the duration of mandatory nap time ranged from as brief as 15 min to approximately 2.5 hr”), and
c) determining whether their nap times are consistent or inconsistent and whether their nap durations remain similar or vary across different napping sessions (Staton, p. 9; “Figure 1 shows the pattern of napping across the three mandatory nap time groups. As demonstrated in the figure, the absolute differences between the curves remained relatively constant”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to extend Irvin’s tracking system to include a designated nap zone as established by Staton. Both Irvin and Staton analyze children participation in early childhood settings. One skilled in the art would recognize that applying Irvin’s zone tracking system to a designated nap zone is a simple adaptation and would allow recording of a child’s nap patterns.
Claim(s) 8, 9, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Irvin et al., (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin in view of Messinger et al. (“Continuous measurement of dynamic classroom social interactions”), hereinafter Messinger.
In regards to claim(s) 8, Irvin discloses that the UWB-RFID tracking captures co-location data for all children in the classroom simultaneously (Irvin, p. 899; “Automated collection, processing, and reporting of these data to teachers could allow them to make more precise data-based decisions… how to best support… child-peer interactions within activity areas.”), but does not explicitly disclose identifying social interactions among children.
However, Messinger discloses tracking every child’s location continuously and compares each child’s contact patterns against every other child in the classroom, identifying social interactions among children, comprising the steps of: a) analyzing children's participation data and comparing them to each other (Messinger, p. 266; “For each pair of children (A and B), we measured the total amount of time TAB they were in social contact (e.g., the total duration of time they were within 1 m of each other).”);
b) measuring duration of simultaneous participation of children within zones (Messinger, p. 263; “This paper explores an alternative approach that captures each child’s interaction with every other child—objective, continuous measurement of classroom behavior using commercially available location-based tracking”);
c) determining, wherein the presence of the respective child with other children in each zone is interpreted as their interaction (Messinger, p. 264 “We illustrate construction of a classroom network based on each child’s time in social contact with every other child.”);
d) determining the percentage of time each child interacts with other children by dividing the duration of time the respective child interacts with each child by the total time the child participates in kindergarten activities (Messinger, p. 266; “We next calculated the social contact frequency… normalized by total observation time
T
. To determine the social contact distribution for each child, we measured their contact frequencies
f
with each of their peers.”);
e) determining a plurality of social groups for each child based on the percentage of time the child interacts with members of each group wherein the social groups comprising: e1) a high-interaction group with 70% interaction or more among the group members; e2) a moderate-interaction group with 50% to 70% interaction among the group members; e3) a casual-interaction group with 30% to 50% interaction among the group members; e4) an occasional-interaction group with 10% to 30% interaction among the group members, and e5) a low-interaction group with less than 10% interaction among the group members (Messinger, p. 266; “Social ties between children were denoted as edges (lines) when that pair of children’s contact frequency was greater than the mean of all pairs on a given day.”) and (Messinger, p. 267; Figure 3 presents rank order plots of each child’s social contact frequency with every peer in the classroom, ranked from most contacted to least contacted). Under the broadest reasonable interpretation, Messinger’s rank order contact distribution establishes the concept of categorizing peers into different tiered grouping structures (e.g., high-interaction, moderate-interaction, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Irvin’s zone-based RFID tracking system with Messinger’s social interaction analysis method because both references operate in the same technological field, and the combination would enable Irvin’s system to not only monitor children’s time within classroom zones but also derive objective measures of peer interaction patterns and social group associations.
In regards to claim(s) 9, Irvin discloses using an RFID system to track each child’s real time location within a classroom from which zone entry and exit events can be derived. Irvin does not explicitly disclose wherein identifying each child's leadership role is based on which child is first to enter or leave a zone, tracking how many children follow that child within a defined time period, or computing a leadership score.
However, Messinger discloses analyzing behavioral relationships and interaction patterns among children using tracked location and proximity data (Messinger, p. 263; “This paper explores an alternative approach that captures each child’s interaction with every other child—objective, continuous measurement of classroom behavior”).
a) determining the count of times a child is the first to leave or enter a zone (Messinger, p. 264; “The technology provides the physical location of each child multiple times per second.”);
b) obtaining the number of children who follow the respective child out of or into the zone within a 3-minute (Messinger, p. 263; “This paper explores an alternative approach that captures each child’s interaction with every other child—objective, continuous measurement of classroom behavior—using commercially available location-based tracking”);
c) calculating a leadership score by dividing the total number of children who follow the respective child by the number of times the child led the entrance and exit zones (Messinger, p. 266; “For each pair of children (A and B), we measured the total amount of time
T
A
B
they were in social contact (e.g., the total duration of time they were within 1 m of each other).”), and
d) identifying the child with the highest leadership score and children with leadership score exceeding half of the highest leadership score as leaders (Messinger, p. 267; Figure 3; “Rank 1 indicates the most contacted peer, Rank 2 the second-most, through the least-most contacted peer.”).
It would have been obvious to one of ordinary skill, in the art as of the effective filing date, to combine the teachings of Irvin and Messinger to arrive at the leadership identification method of the claimed invention. Messinger discloses analyzing behavioral relationships using real-time location tracking data similarly to Irvin’s real-time tracking of children’s movement within classroom zones. One of ordinary skill in the art would recognize that incorporating Messinger’s teachings with Irvin’s zone defined participation tacking system would allow for identification of individual leadership roles.
In regards to claim(s) 12, Irvin discloses automated collection, processing, and reporting of child movement and location data across classroom activity areas (Irvin, p.899; “RTLS automated collection, processing, and reporting of child movement data promises to allow teachers and allied health services providers (e.g., occupational therapists) to make more precise data-based decisions about classroom arrangements, identification of disabilities, and how best to support adult-child and child-peer interactions within activity areas.”). Irvin does not explicitly disclose, a chart determining the impact values of zones and subdomains on the development criteria, and an optimizing kindergarten setting map.
However, Messinger discloses that construction of per-child social interactions maps social network visualizations from an RFID location data (Messinger, p. 269; “analyses provide an overview of the web of social contacts in the classroom and can be subject to a wide range of analyses focusing on the connectivity of individual children, groups of children,”), and the report of the social contact data underlying these maps show a quantified heterogeneity in individual children’s social preferences (Messinger, p. 267; Figure 3, “rank 1 indicates the most contacted peer, rank 2 the second-most”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate Messinger’s social network visualization method into Irvin’s classroom participation reporting system to provide a report of child-peer interactions within the tracked classroom environment. Irvin and Messinger both disclose location tracking data to evaluate children in early childhood education settings. One of ordinary skill in the art would recognize that combining the Irvin and Messinger’s teachings would allow for visualization and interpretation of participation relationships, developmental patterns, and classroom interaction data within early childhood settings.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Irvin et al, (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin in view of Staton et al., (“Mandatory Nap Times and Group Napping Patterns in Child Care: An Observational Study”) hereinafter Staton, further in view of Smith et al., (“Correlates of naptime behaviors in preschool aged children”) hereinafter Smith, and further in view of Mahone et al (“Assessment of Attention in Preschoolers”), hereinafter Mahone.
In regards to claim(s) 10, Irvin discloses a) Identifying potential problems and strengths in children's total duration of attention (Irvin, p. 890; “Performing ongoing data collection is valuable in that it allows teachers to monitor children’s progress and initiate specific interventions when children are not making developmental improvements”),
Irvin broadly discloses tracking children’s developmental progress but does not explicitly disclose establishing age-appropriate attention thresholds or categorizing children into three groups with respect to attention duration. However, Mahone discloses that attention performance is directly age dependent.
a1) establishing a range of age-appropriate duration of attention for each child (Mahone, p. 11; “Performance (omissions, response latency, and variability) improved with age from age 3–4 years, leveling off by age 5 years and was correlated with parent ratings on the CPRS-R”);
a2) comparing children's total duration of attention with their range of age-appropriate duration of attention (Mahone, p. 24; “The benefit of the rating scales is that the severity of inattention symptoms can be directly compared to “typical” attention problems observed in this age range”);
a3) categorizing children based on the comparison in step b into three groups comprising: i) children with total duration of attention shorter than their age- appropriate range (Mahone, p. 2; “preschool children presenting with disrupted attentional skills are at significant risk for social, developmental, and academic difficulties, relative to typically developing children”); ii) children with total duration of attention longer than their age- appropriate range (Mahone, p. 10; “Preschool children without behavioral difficulties are observed to have fewer omissions, lower reaction time standard error, less variability and less deterioration in performance over time than children with hyperactive and oppositional behaviors”), iii) children with total duration of attention within their age- appropriate range, and wherein children are categorized into groups i and ii could indicate potential problems and strengths (Mahone, p. 1, Abstract; “While inattention among preschoolers is common, symptoms alone do not necessarily indicate a disorder, and most often represent a normal variation in typical preschool child development”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Mahone’s age-appropriate attention evaluation framework with Irvin’s tracking system to identify developmental strengths and potential attention related problems in early childhood settings. Irvin discloses the importance of collecting zone-based data to monitor developmental progress and initiate interventions. Mahone discloses that attention performance is age dependent and should be compared against age-appropriate norms to distinguish normal development variation from potential attention problems. One of ordinary skill in the art would recognize that combining Mahone’s age range reference comparison and categorization methods with Irvin’s system would allow for the collected data to be evaluated for age-appropriate development expectations.
Irvin does not disclose b) Identifying potential problems and strengths in children's napping patterns. However, Smith discloses a classification of preschool children into distinct napping behavioral groups based on their teacher reported responses to naptime (Smith, Abstract; “This study sought to identify factors that discriminate between four groups of children with different teacher-reported responses to naptime in childcare: those who nap (nappers), sometimes nap (transitioners), do not nap (resters), and neither nap, nor lie still (problem nappers).”); Smith further discloses that these age groups are differentiated by cognitive functioning and nighttime sleep duration, thereby teaching b1) identifying each child's total post-nap duration of attention; b2) identifying each child's total pre-nap duration of attention; that these groups are differentiated by cognitive functioning and nighttime sleep duration; b3) determining whether each child exhibits high or low post-nap participation, wherein high post-nap participation indicates a longer total post-nap duration of attention compared to total pre- nap duration of attention and total duration of attention and wherein low post-nap participation indicates a shorter total post- nap duration of attention compared to their total pre-nap duration of attention and total duration of attention; (Smith, p. 31; “results revealed that nappers were younger, had poorer cognitive functioning, and shorter nighttime sleep duration than non-nappers (ie, resters and problem nappers) and children who were transitioning away from napping (ie, transitioner”)). Under the broadest reasonable interpretation, Smith’s measurement of cognitive and behavioral states across napping groups cover identifying attention duration before and after nap periods (Smith, p. 30-31; Table 1 and Table 2).
It would have been obvious to one of ordinary skill, in the art as of the effective filing date, to incorporate Smith’s pre/post-nap behavioral classification framework with Irvin’s zone tracking system to identify each child’s attention duration before and after a designated nap zone. Irvin discloses collection of per child participation data across classroom activities. Smith discloses evaluating behavioral and cognitive differences associated with nap patterns. One of ordinary skill in the art would recognize that combining Smith’s teachings with Irvin’s tracking system for recording per-child engagement data across zones provides the necessary inputs to assess pre/post-nap attention measurements.
Irvin does not explicitly disclose categorizing children into groups defined by the combination of whether their nap times are consistent or inconsistent, whether nap duration is similar or variable across sessions, and whether post-nap participation is high or low. However, Staton discloses that mandatory nap time duration was categorized in three groups (Staton, p. 5; “the continuous lengths of mandatory nap times were categorized into three groups; ≤ 30, 31–60, > 60.”) and that children’s napping behavior patterns differ systematically across grouped defined by mandatory nap time duration, and that these patterns remain consistent in their relative differences across observation time (Staton, Figure 1 and p. 9-10; “the absolute differences between the curves remained relatively constant, with no evidence that nap onset latency differed between the three mandatory nap time groups.”). Under the broadest reasonable interpretation, Staton’s nap duration groupings and pattern consistency analysis teach b4) categorizing children into eight groups comprising: i) children with consistent nap times of similar duration and high post-nap participation; ii) children with consistent nap times of variable duration and high post-nap participation; iii) children with inconsistent nap times of similar duration and high post-nap participation; iv) children with inconsistent nap times of variable duration and high post-nap participation; v) children with consistent nap times of similar duration and low post-nap participation; vi) children with consistent nap times of variable duration and low post-nap participation; vii) children with inconsistent nap times of similar duration and low post-nap participation; viii) children with inconsistent nap times of variable duration and low post-nap participation, and wherein children are categorized into groups could indicate potential problems and strengths.
It would have been obvious to one of ordinary skill in the art to combine Staton’s nap duration and consistency framework with Irvin’s zone tracking system. Irvin’s system already captures the specific entry times, durations and frequencies, and the provides the raw parameters from which nap consistency and variability are directly computable. Staton establishes that categorizing children by these specific patterns is a recognized analytical standard. One of ordinary skill in the art would recognize that applying Staton’s classification framework to Irvin’s per child tracking dataset would allow for automated classification of children’s nap behavior patterns.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Irvin et al., (“An automated approach to measuring child movement and location in the early childhood classroom”), hereinafter Irvin in view of Neitzel et al., (“Children’s Early Interest-Based Activities in the Home and Subsequent Information Contributions and Pursuits in Kindergarten”) hereinafter Neitzel,
In regards to claim(s) 11, Irvin teaches the method further comprises the step of evaluating the impact of zone-based kindergarten settings and proposing redesign suggestions using the participation data (Irvin, p.899; “RTLS automated collection, processing, and reporting of child movement data promises to allow teachers… to make more precise data-based decisions about classroom arrangements) comprising the steps of:
a) determining total time spent by children in each of the plurality of zones and total time spent by children in each of the plurality of subdomains within a zone (Irvin, Abstract; “(a) determining the activity areas where children are spending the most and least time per day”);
c) identifying a plurality of redesign suggestions for zone and subdomain organization based on the participation percentages of zones and subdomains and their impact value on developed, underdeveloped, and overdeveloped criteria, wherein the redesign suggestions aim to maintain participation percentages for zones and subdomains impacting developed criteria and improve participation percentages for zones and subdomains impacting underdeveloped criteria (Irvin, p. 891-892; “The ability to move among specific classroom activity areas and spend time in these activity areas is believed to selectively address core difficulties more so than others… and classroom location data can assist teachers… in designing optimal learning environments”);
d) generating redesign suggestions comprising at least one of the following (Irvin, p. 899; “make more precise data-based decisions about classroom arrangements,”);
d1) rearranging the positions of zones within the kindergarten setting (Irvin, p. 891-892; “classroom location data can assist teachers and researchers in designing optimal learning environments”); Irvin discloses that participation data is used to derive classroom and zone arrangement recommendations, because the claim requires at least one of d1-d3, disclosure of d1 alone satisfies the limitation.
Irvin discloses the participant per-zone total time data (Irvin, p. 896; Table 3) but does not explicitly disclose b) calculating participation percentages for each of the plurality of zones and each of the plurality of subdomains, wherein participation percentages for each zone are calculated by dividing the total time spent in the respective zone by the total time spent in all zones and for each subdomain are calculated by dividing the total time spent in the respective subdomain by the total time spent in all subdomains.
However, Neitzel discloses calculating and comparing activity domain participation percentages as the standard analytical method in early childhood participation (Neitzel, p. 785; “We calculated the percentage of activities within each of the types by dividing the number of reported activities aligned with that type by the total number of reported activities for the year.”). It would have been obvious to one of ordinary skill, in the art as of the effective filing date, to apply Neitzel’s participation percentage calculations to Irvin’s per zone participation dataset. Irvin already provides per-zone total time data which the percentage calculations disclosed by Neitzel would be a direct computation, as both references analyze children participation in early childhood setting.
In regards to claim(s) 13, Irvin discloses optimizing kindergarten setting map comprising the participation percentages for zones and subdomains, developed, underdeveloped, and overdeveloped criteria, and redesign suggestions (Irvin, p.; “classroom location data can assist teachers and researchers in designing optimal learning environments”).
Conclusion
Accordingly claims 1-13 are rejected.
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/B.M./Examiner, Art Unit 3715
/KANG HU/ Supervisory Patent Examiner, Art Unit 3715
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