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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/08/2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 and 6-9 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes. Claim 1 is drawn to a system, i.e. a machine or manufacture.
Step 2A, Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes. Claim 1 recites the following abstract ideas:
“acquire presence/absence information indicating a presence/absence of a spouse of a target user, a child of the target user, or a parent of the target user…wherein the presence/absence of the spouse of the target user, or the child of the target user, or the parent of the target user is not already stored in the at least one memory” – This is an observation, evaluation, judgement, or opinion performable in the human mind. For example, the human mind is capable of acquiring visual information regarding the presence or absence claimed.
“acquire household information indicating a household including the target user and one or more family users” – This is an observation, evaluation, judgement, or opinion performable in the human mind. For example, the human mind is capable of ingesting, i.e. acquiring, information regarding a household by reading, for example.
“acquire relationship information indicating a type of a relation between the target user and each of the one or more family users” - This is an observation, evaluation, judgement, or opinion performable in the human mind. For example, the human mind is capable of acquiring relationship information via reading comprehension.
“identify, based on the acquired relationship information, from among the one or the plurality of family users included in the household indicated by the household information, a family user which is one of the spouse, the child, and the parent indicated to be present by the presence/absence information” - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. For example, the human mind is capable of identifying a person or persons and their relationship to another person or persons.
Step 2A, Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No. Claim 1 recites the following additional elements:
“at least one processor” – This corresponds to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
“at least one memory device that stores a plurality of instructions which, when executed by the at least one processor, cause the at least one processor to…” – This corresponds to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
“store, among the spouse, the child, or the parent of the target user of which the presence/absence information indicates a presence, the spouse the child or the parent for which a corresponding family user is not identified as a new related user belonging to the household in a storage unit in associated with relationship information indicating a type of a relation between the new related user and the target user” - This corresponds to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No. Claim 1 recites the following additional elements:
“at least one processor” – This corresponds to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
“at least one memory device that stores a plurality of instructions which, when executed by the at least one processor, cause the at least one processor to…” – This corresponds to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
“store, among the spouse, the child, or the parent of the target user of which the presence/absence information indicates a presence, the spouse the child or the parent for which a corresponding family user is not identified as a new related user belonging to the household in a storage unit in associated with relationship information indicating a type of a relation between the new related user and the target user” - This corresponds to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).
Claims 8-9 are ineligible for the same reasons pertaining to claim 1.
Claims 2-4 and 6-7 either recite additional abstract ideas (identifying, estimating, and storing, which are all performable in the human mind) or further expound on the source of information for the abstract ideas, i.e. what the observations, evaluations, judgements, or opinions are “based on”. Judicial exceptions cannot be overcome by the addition of judicial exception. Claims 2-4 and 6-7 are ineligible.
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.
Claim(s) 1-4 and 6-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bruich (US20130151527A1).
Regarding claim 1, Bruich teaches an information processing system (see ¶59-62 and below), comprising:
at least one processor (see ¶59-62); and
at least one memory device that stores a plurality of instructions which, when executed by the at least one processor, causes the at least one processor to (see ¶59-62):
acquire presence/absence information indicating a presence/absence of a spouse of a target user, a child of the target user, or a parent of the target user (¶24, “The household prediction module 114 may use one or more fields in the user profile objects 102 associated with users that indicate familial relationships, such as being a son or daughter of one or more users, being married to a user, being the parent of one or more children, and so on.”);
acquire household information indicating a household including the target user and one or a plurality of family users (¶24, “The household prediction module 114 may use one or more fields in the user profile objects 102 associated with users that indicate familial relationships, such as being a son or daughter of one or more users, being married to a user, being the parent of one or more children, and so on.”);
acquire relationship information indicating a type of relation between the target user and each of the one or the plurality of family users (¶24, “The household prediction module 114 may use one or more fields in the user profile objects 102 associated with users that indicate familial relationships, such as being a son or daughter of one or more users, being married to a user, being the parent of one or more children, and so on.”); and
store, among the spouse, the child, or the parent of the target user of which the presence/absence information indicates a presence, the spouse, the child, or the parent for which a corresponding family user is not identified as a new related user belonging to the household in a storage unit in association with relationship information indicating a type of a relation between the new related user and the target user (¶44 and Figure 4, 402-412, “The statistical analysis module 302 analyzes received information about users on the social networking system 100 to predict household memberships of the users. In one embodiment, the statistical analysis module 302 analyzes the information received about a user and searches for one or more households that match the user. Using factors such as previous names, date of birth, location, life events, and check-ins, potential households may be filtered to match the user. The statistical analysis module 302 may determine a probability that the user is a member of a household based on these factors. In one embodiment, social graph information is used to further determine a probability that a user is a member of a household based on other members of the household and whether they are close connections to the user or whether they have a familial relationship with the user. Statistical analysis may also be performed by the statistical analysis module 302 to determine a probability, based on the received information about the user linking the user to the household, that the user is a member of the household based on past predictions and historical household data.”);
wherein the presence/absence of the spouse of the target user, or the child of the target user, or the parent of the target user is not already stored in the at least one memory (see ¶24, “The household prediction module 114 may use one or more fields in the user profile objects 102 associated with users that indicate familial relationships, such as being a son or daughter of one or more users, being married to a user, being the parent of one or more children, and so on.” The presence/absence information does not have to include all of the spouse, child, and parent, but can be just one, or one or more of them. Therefore, in the case that not all presence/absence indications are stored, for example if only spouse and child presence/absence information is stored because there is no parent presence/absence information, then parent presence/absence information would not be “already stored in the at least one memory”);
identify, based on the acquired relationship information, from among the one or the plurality of family users included in the household indicated by the household information, a family user which is one of the spouse, the child, and the parent indicated to be present by the presence/absence information (¶24, “The household prediction module 114 may use one or more fields in the user profile objects 102 associated with users that indicate familial relationships, such as being a son or daughter of one or more users, being married to a user, being the parent of one or more children, and so on. For example, a user with a child under the age of 18 may be assumed to live with the child. Thus, the user profile objects 102 associated with the parent and child may be categorized as a family household. This household classification may, in one embodiment, be used to assign users to households gathered from external household data 110.”)
Regarding claim 2, Bruich teaches all of the limitations of claim 1, wherein
the instructions further cause the at least one processor to
identify, from a plurality of users registered in a user database, the one or more family users who are included in the household, including the target user, and who live together with the target user (“Finally, social graph correlation may indicate whether the social relationship indicated in a household matches the social graph relationship of users in the social networking system 100. For example, a household that includes two members, a married couple, may be associated with two users that have indicated they are married to each other. The social graph correlation would be 1.0 because the users social graph relationship, their married status, correlates directly with the social graph relationship indicated in the household. One or more scoring models may be generated to determine confidence scores for assigning users to households based on these correlations.”)
Regarding claim 3, Bruich teaches all of the limitations of claim 1, wherein the instructions further cause the at least one processor to:
estimate, based on an output of a machine learning model when a value of an input parameter relating to the target user is input to the machine learning model (¶46, “For example, a household with a thirty-four year old male married to a thirty-three year old female that subscribes to Sports Illustrated and Vogue magazines in Mountain View, Calif. may be matched to a married couple of users that have matching user attributes of age, gender, social graph relationship status, and interests in sports and fashion. These factors may be weighted differently in a scoring model to determine a confidence score for assigning users to households. An initial set of weights may be assigned by administrators of the social networking system 100. The weights may be adjusted over time using machine learning algorithms based on user feedback and information received about the accuracy of the household predictions, in one embodiment. In another embodiment, the weights may be adjusted using machine learning methods based on data gathered by the social networking system 100.”), trained by using learning including values of input parameters determined in advance and relating to users, the presence/absence of the spouse of the target user, the child of the target user, or the parent of the target user, and to acquire the presence/absence information which indicates a result of the estimation ( ¶46, “For example, a household with a thirty-four year old male married to a thirty-three year old female that subscribes to Sports Illustrated and Vogue magazines in Mountain View, Calif. may be matched to a married couple of users that have matching user attributes of age, gender, social graph relationship status, and interests in sports and fashion. These factors may be weighted differently in a scoring model to determine a confidence score for assigning users to households. An initial set of weights may be assigned by administrators of the social networking system 100. The weights may be adjusted over time using machine learning algorithms based on user feedback and information received about the accuracy of the household predictions, in one embodiment. In another embodiment, the weights may be adjusted using machine learning methods based on data gathered by the social networking system 100.”)
Regarding claim 4, Bruich teaches all of the limitations of claim 3, wherein
the machine learning model includes:
a plurality of functions determined in advance and configured to output a score relating to whether the spouse of the target user, the child of the target user, or the parent of the target user is present based on one or a plurality of the input parameters each relating to a user (¶48, “Scoring models used by the confidence scoring module 306 may use various factors in determining whether a user is a member of a household, including date of birth, previous names, location, check-in events, timeline data, social graph information, and interests. Weights may be used emphasize one factor over another, as determined by administrators of the social networking system 100. Weights may also be trained by machine learning algorithms based on received information linking users of the social networking system 100 to households.”); and wherein the instructions further cause the at last one processor to
estimate the presence/absence of the spouse of the target user, the child of the target user, or the parent of the target user based on outputs of the plurality of functions and weights of the plurality of functions determined by learning, and to determine the presence/absence information indicating a result of the estimation (¶47, “A confidence scoring module 306 generates a confidence score for each household membership prediction generated by the household prediction module 114. A confidence score may be determined based on a scoring model for the household predictions. Confidence scores may range from 0 to 100. A low confidence score, such as 10 or 15, may indicate that there are multiple households in which a user may be associated.”, ¶44 “The statistical analysis module 302 analyzes received information about users on the social networking system 100 to predict household memberships of the users. In one embodiment, the statistical analysis module 302 analyzes the information received about a user and searches for one or more households that match the user. Using factors such as previous names, date of birth, location, life events, and check-ins, potential households may be filtered to match the user. The statistical analysis module 302 may determine a probability that the user is a member of a household based on these factors. In one embodiment, social graph information is used to further determine a probability that a user is a member of a household based on other members of the household and whether they are close connections to the user or whether they have a familial relationship with the user. Statistical analysis may also be performed by the statistical analysis module 302 to determine a probability, based on the received information about the user linking the user to the household, that the user is a member of the household based on past predictions and historical household data.”)
Regarding claim 6, Bruich teaches all of the limitations of claim 1, wherein the instructions further cause the at least one processor to estimate
based on information relating to the target user, an age of the spouse, the child, or the parent for which a corresponding family user is not identified (¶13, “As a result, a social networking system may infer certain profile attributes of a user, such as geographic location, educational institutions attended, and age range, by analyzing the user's connections and their declared profile information.”)
Regarding claim 7, Bruich teaches all of the limitations of claim 1, wherein
the relationship information is based on at least one of a surname, an IP address, a street address, an age difference, or a gender are acquired (¶14, “In one embodiment, a user's IP address may be mapped to a particular geographic region by the social networking system. Location information about other users connected to a user on the social networking system may be used to infer the location of the user.”, ¶18, “For example, a user may be "checked-in" by another user of the social networking system 100 using a GPS-enabled user device. As a result, the location information captured by the GPS-enabled user device may be stored in the edge object 106 between the users of the social networking system 100. Further, the edge object 106 between the connected users included in the check-in event may indicate that the connection is stronger than the connection between users that do not interact with each other as frequently. This type of connection information, extracted from edge objects 106, may be used by the social networking system 100 in assigning users of the social networking system 100 to households.”); wherein
the family user corresponding to the spouse, the child, or the parent indicated to be present by the presence/absence information is identified based on the acquired type of the relation between the target user and each of the one or more family users (“Finally, social graph correlation may indicate whether the social relationship indicated in a household matches the social graph relationship of users in the social networking system 100. For example, a household that includes two members, a married couple, may be associated with two users that have indicated they are married to each other. The social graph correlation would be 1.0 because the users social graph relationship, their married status, correlates directly with the social graph relationship indicated in the household. One or more scoring models may be generated to determine confidence scores for assigning users to households based on these correlations.”)
Regarding claim 8, Bruich according to claim 1 and/or claims 2-7 performs the method of claim 8 under normal operation.
Regarding claim 9, Bruich according to claim 1 covers the functionality of claim 9 and the implementation on a non-transitory computer readable storage medium storing a plurality of instructions, wherein the executed by at least one processor, the plurality of instructions cause the at least one processor to perform the functionality, see at least ¶58-63.
Response to Arguments
Applicant’s arguments filed 03/08/2026 have been fully considered.
With regard to rejections under 35 USC 101, Applicant contends “A person of ordinary skill in the art would understand that training a machine learning model requires ingesting and processing enormous datasets-often millions or billions of examples (e.g., images, text tokens). A human mind cannot hold or iterate over such volumes. Human working memory is limited to about 7 (plus/minus 2 items)¹, and long-term recall is lossy and abstracted. Machine learning models process data verbatim as replicas, not abstractions like human perception. In contrast, humans abstract and forget most details, making precise, exhaustive training impossible mentally.”
Claim 1 does not recite a machine learning model and therefore the argument is moot with respect to claim 1.
Claim 3 passively recites a trained machine learning model. It is never asserted that the training of a machine learning model is a mental process performable in the human mind. Claim 3 recites estimation based on the outcome of a machine learning model, i.e. the outcome is taken and used for estimation. The machine learning model is merely a source of information for estimation.
Notwithstanding, the claims are not limited to the ingestion and processing of “millions or billions of examples” or of “enormous datasets”. Machine learning is not inherently restricted to the ingestion and processing of “enormous datasets” or those containing “millions or billions of examples”.
Applicant has argued that the claimed invention improves upon data processing for social graph analysis. However, the claimed invention merely recites the functionality and/or outcome of the methodology without providing any concrete steps which improve data processing for social graph analysis. Applicant contends “The claimed system improves the technical field of data processing for social graph analysis by providing a detailed and automated method to map household compositions, which prior machine learning based systems (as noted in paragraphs [0003]-[0005]) could not achieve. This addresses a specific technical problem: the inability to comprehensively determine detailed household relationships using existing data analysis methods.” However, none of the steps of acquiring, storing, or identifying, which ostensibly would provide the improvement, are claimed so as to reveal any internal workings which provide an improvement to the function of a computer or to the data processing, i.e. none of the processing associated with the claimed outcomes from acquiring, storing, or identifying are claimed.
Applicant cites a recent appeal decision authored by Director Squires which notes that considering any software algorithm as “generic computer components” eschews “the clear teachings” of precedent. In the present action, software algorithms are not declared to be “generic computer components”. Generic computer components are claimed which are used to merely apply the abstract ideas recited in the claims. Applicant responds by citing a portion of the decision, “Yet, under the panel's reasoning, many AI innovations are potentially unpatentable- even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm"…” As described above, the crux of the issue is that the asserted AI innovation is not adequately described. There are steps recited in the claims and their outcomes are noted but nothing is claimed which provides adequate description as to the innovation involved in the steps.
Applicant contends “…the amended passage of independent claim 1, where a "user is not already stored in the at least one memory", cannot be disclosed by Bruich.” However, as shown herein, the claim can be interpreted such that Bruich discloses this feature.
Conclusion
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/SCHYLER S SANKS/ Primary Examiner, Art Unit 2129
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