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 . Claims 1-11 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non- Final Rejection Applicant amended claims. Claims 1-11 are pending and have been rejected below.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2021-165641, filed on 10/07/2021.
Drawings
The drawings submitted on 04/04/2024 have been reviewed and are considered acceptable.
Response to Amendment
Applicant’s amendments are received and acknowledged.
Claim Objections
Claims 1, 10, and 11 are objected as they recite: collecting, for each of a plurality of users, household job data of the user on the basis of the use information. The claim introduces a plurality of users, then refers to the user. The Examiner interprets this as a minor informality wherein the Applicant did not recite “the users.” For purposes of examination, the Examiner interprets the user to be the users.
Appropriate correction is required.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that claims are directed towards a specific technological improvement and does not recite certain methods of organizing human activity nor mental processes.
Examiner respectfully disagrees. The claims are directed towards acquiring use information, collecting household job data, acquiring third party coefficient information, and calculating a household job point commensurate with a household job all of which are concepts capable of being performed in the human mind (i.e. via pen and paper). Further the claims are directed towards sharing jobs involving a group of users. (See Specification, [08]) which would fall under the abstract idea category of certain methods of organizing human interactions.
Applicant further points to Enfish… and McRO… asserting claims recite a specific improvement to computer functionality and claims are directed towards a rule0based automation method.
Examiner respectfully disagrees. The present claims are not analogous to either of the cited cases. Enfish is directed towards a self-referential database having two key features: all entity types can be stored in a single table; and the table rows can contain information defining the table columns, while McRO… is directed towards directed towards lip synchronization in computer animation. The present claims do not recite any such elements but are directed toward acquiring use information, collecting household job data, acquiring third party coefficient information, and calculating a household job point commensurate with a household job.
Applicant contends at Step 2A, Prong 2 that solves a real-world problem of imbalanced housework distribution. Applicant further points to DDR Holdings… asserting that the claim solve a technical problem.
Examiner respectfully disagrees. The cited case in not analogous to the present application. DDR Holdings… is directed towards is directed towards a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage and improved, particular methods of digital data compression while the present application is directed to the limitations cited above.
Applicant contends similar to CardioNet… the claims are directed to the functioning of a data processing system.
Examiner respectfully disagrees. CardioNet… is not analogous to the present claims as the claims is directed towards identifying heart beats in a sensed cardiac signal and activating a T wave filter, using said heartbeats in response to a message, while the present claims are directed towards the abstract idea recited above.
Applicant contends at Step 2B that the claims recite significantly more than the abstract idea and further points to Bascom… as the claims recite a non-conventional and non-generic arrangements of known conventional pieces.
Examiner respectfully disagrees. The present claims are not analogous to those of Bascom… as they recite generating network access requests, remote ISP servers associating network accounts, filtering schemes, and filtering elements. The present claims do not recite any such elements as recited by the cited cases above.
The 101 Rejection is updated and maintained below.
Response to Arguments - 35 USC § 102/103
Applicant’s amendments have overcome the 102 rejection, but facilitate a new 103 rejection. The Applicant’s arguments are moot in line of the new line of 103 Rejection below.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are:
the acquisition part acquires use information… (Claim 10)
the processing part collects household job data… (Claim 10)
the acquisition part acquires third party coefficient information… (Claim 10)
the processing part calculates… (Claim 10)
The specification does provide corresponding structure of the acquisition part acquires use information… (Claim 10), the processing part collects household job data… (Claim 10), the acquisition part acquires third party coefficient information… (Claim 10), the processing part calculates… (Claim 10). The specification does provide structure for the above elements (Specification, [44]; The controller 111 operatively includes an acquisition part 201, a processing part 202, an output part 203, and a manufacturer coefficient setting part 204 each coming into effect when a CPU executes a program read to a ROM or a RAM from a storage part 112. In other words, the program causes the information processing device 11 to serve as the acquisition part 201, the processing part 202, the output part 203, and the manufacturer coefficient setting part 204).
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) 1-11 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1, 10, and 11 recite a series of steps for the abstract idea recited below.
Regarding Claims 1, (additional elements bolded)
A household job point calculation method, comprising: by an information processing device, acquiring use information indicating a use situation of an appliance or a facility;
collecting, for each of a plurality of users household job data of the user on the basis of the use information;
acquiring third party coefficient information indicating a third party coefficient set by a third party different from the plurality of user; and
calculating for each of the plurality of users, on the basis of the household job data and the third party coefficient information, a household job point that is commensurate with a household job amount of the user.
Regarding Claims 10,
An information processing device, comprising:
an acquisition part; and
a processing part, wherein
the acquisition part acquires use information indicating a use situation of an appliance or a facility,
the processing part collects for each of the plurality of users household job data of a user on the basis of the use information,
the acquisition part acquires third party coefficient information indicating a third party coefficient set by a third party different from the plurality of user, and
the processing part calculates, for each of the plurality of users, on the basis of the household job data and the third party coefficient information, a household job point that is commensurate with a household job amount of the user.
Regarding Claims 11,
A computer-readable non-transitory recording medium recording a program causing an information processing device to serve as an acquisition part and a processing part, wherein
the acquisition part acquires use information indicating a use situation of an appliance or a facility,
the processing part collects, for each of a plurality of users household job data of the user on the basis of the use information,
the acquisition part acquires third party coefficient information indicating a third party coefficient set by a third party different from the plurality of users, and
the processing part calculates, for each of the plurality of users on the basis of the household job data and the third party coefficient information, a household job point that is commensurate with a household job amount of the user.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards acquiring use information, collecting household job data, acquiring third party coefficient information, and calculating a household job point commensurate with a household job all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards sharing jobs involving a group of users. (See Specification, [08]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least an information processing device; an acquisition part; a processing part; A computer-readable non-transitory recording medium recording a program causing an information processing device to serve as an acquisition part and a processing part. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claim(s) 2-9, the claim further narrows the abstract idea or recite additional elements previously rejected in the independent claims.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 10, and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takayama et al. (JP 2004272650 A) in view of Marti et al. (US 20160132030 A1).
Regarding Claim(s) 1, 10, and 11, Takayama teaches: A household job point calculation method, comprising: by an information processing device, acquiring use information indicating a use situation of an appliance or a facility; (Takayama, [06]; the present invention is an electronic device that acquires, accumulates, and outputs operation information that is information relating to the operation of an electronic device such as a home appliance and Takayama, [39]; The operation information configuration unit of the washing machine 303 acquires the operation start time when the start button or the like of the washing machine 303 is input and the operation end time when the end button or the like is input. The difference between the operation end times is obtained, and the operation time is calculated). Examiner notes that Takayama teaches a variety of appliances washing machine, vacuum, etc. (Takayama, [35]).
collecting… household job data of the user on the basis of the use information; (Takayama, [43]; the operation information storage unit stores the received operation information in association with the date and time information. That is, the operation information accumulation unit accumulates the first operation information, which is information of the cleaner 302, in association with the first date and time information. The second operation information and the second date and time information, which are information on the washing machine 303, are stored in association with each other and Takayama, [99]; the vacuum cleaner that receives the location information and the cleaner identification information and transmits the information to the information processing apparatus, the location information and the cleaner identification information from the vacuum cleaner, and the location information and the cleaner identification. An information processing system having an information processing device that obtains working hours from information, wherein the information processing system can automatically and accurately calculate a fee for domestic work. Further, by outputting the location information, the cleaner identification information, and the labor fee, it is possible to visually distinguish between a cleaned location and a non-cleaned location. In addition, it is possible to accurately evaluate work and the like of a housewife, housekeeper, cleaner, etc. at home).
acquiring third party coefficient information indicating a third party coefficient set by a third party different from the… user; and (Takayama, [44]; when the clock of the information processing apparatus 301 reaches the calculation start time, the labor charge calculation unit acquires the first operation information from the operation information storage unit, and based on the first operation information, Is calculated. The calculation start time is 21:00. Specifically, the labor rate calculation unit uses the identification number and the operation mode of the first operation information as search keys to search for the per-unit-time charge that is recorded in advance in a memory built in the information processing device and Takayama, [46]; The charge per unit time differs for each electronic device (identification number), and also differs depending on the operation mode and the like and Takayama, [50]; the charge per unit time is recorded in advance in a memory built in the information processing apparatus. However, the charge per unit time is transmitted from an external device to It can be customized, such as by the device acquiring or manually entering a fee per unit time and Takayama, [76]; The labor fee calculation unit 10023 searches for, for example, the data of the location identification number and the labor fee recorded in a memory built in the information processing device 10021 in advance using the location identification number of the location information as a search key. As a result of this search, the labor fee of the corresponding place identification number is obtained). Examiner interprets the labor fee associated with the location identification number as the third party coefficient.
calculating…, on the basis of the household job data and the third party coefficient information, a household job point that is commensurate with a household job amount of the user. (Takayama, [45]; the labor fee calculation unit calculates the labor fee by multiplying the acquired fee per unit time by the operation time of the operation information. In FIG. 8, the charge per unit time is “2,000”, and the labor charge “6,000” is calculated by multiplying the “2,000” by the operation time “3”.
While Takayama teaches collecting user use information, acquiring user coefficients, and calculating a user job point, Takayama does not appear to teach a plurality of users. However, Takayama in view of the analogous art of Marti (i.e. home monitoring) does teach a plurality of users (Marti, [06]; One or more controller devices can also act as a “coordinator” to manage communications between multiple controllers and multiple accessories and Marti, [20]; A coordinator can receive information about detected patterns from the mobile devices and can analyze the information to detect an aggregate pattern (i.e., a pattern involving multiple mobile devices and/or multiple users). Based on a detected aggregate pattern, the coordinator can identify an operational behavior of one or more accessories to automate (e.g., turn off the lights when the last user goes to bed) and can implement the automated behavior, e.g., by establishing an automation rule that reflects the detected aggregate pattern).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Takayama including collecting user use information, acquiring user coefficients, and calculating a user job point, with the teachings of Marti including a plurality of users in order to determine usage patterns (Marti, [89]; Coordinator 604 can analyze the pattern data received from multiple mobile devices 610 to detect aggregate patterns across the users who frequent automated environment 602. For example, coordinator 604 can include its own pattern analysis module 622, which can implement algorithms similar to pattern analysis subsystem 506 of FIG. 5. Pattern analysis module 622 can operate on shared pattern results stored in data store 620 and can detect aggregate patterns across the users (or their controllers). As used herein, an “aggregate pattern” can include any pattern that incorporates behaviors of multiple mobile devices and/or multiple users).
Further regarding Claim(s) 10, Takayama teaches: An information processing device, comprising: an acquisition part; and a processing part, (Takayama, [06]; the present invention is an electronic device that acquires, accumulates, and outputs operation information that is information relating to the operation of an electronic device such as a home appliance and Takayama, [39]; The operation information configuration unit of the washing machine 303 acquires the operation start time when the start button or the like of the washing machine 303 is input and the operation end time when the end button or the like is input. The difference between the operation end times is obtained, and the operation time is calculated). Examiner notes that “parts” can be part of the information processing device (Specification, [44]; In other words, the program causes the information processing device 11 to serve as the acquisition part 201, the processing part 202, the output part 203, and the manufacturer coefficient setting part 204).
Further regarding Claim(s) 11, Takayama teaches: A computer-readable recording medium recording a program causing an information processing device to serve as an acquisition part and a processing part, (Takayama, [54]; in the present embodiment, in the information processing apparatus, the operation information storage unit stores the operation information and the date and time information. However, another information processing apparatus uses the stored operation information and the date and time information. In this case, the operation information storage unit may have a recording medium such as a DVD or an IC card, move the recording medium, and output the operation information by another information processing device. Further, the information processing apparatus may be configured to include a network card and its driver software, and transmit the operation information and the date and time information stored in the operation information storage unit to another information processing system and (Takayama, [06]; the present invention is an electronic device that acquires, accumulates, and outputs operation information that is information relating to the operation of an electronic device such as a home appliance and Takayama, [39]; The operation information configuration unit of the washing machine 303 acquires the operation start time when the start button or the like of the washing machine 303 is input and the operation end time when the end button or the like is input. The difference between the operation end times is obtained, and the operation time is calculated). Examiner notes that “parts” can be part of the information processing device (Specification, [44]; In other words, the program causes the information processing device 11 to serve as the acquisition part 201, the processing part 202, the output part 203, and the manufacturer coefficient setting part 204).
Regarding Claim(s) 2, Takayama teaches: The household job point calculation method according to claim 1, wherein the third party includes a manufacturer of an appliance or a facility. (Takayama, [76]; The labor fee calculation unit 10023 searches for, for example, the data of the location identification number and the labor fee recorded in a memory built in the information processing device 10021 in advance using the location identification number of the location information as a search key. As a result of this search, the labor fee of the corresponding place identification number is obtained). Examiner interprets the location associated with the location identification number and corresponding labor fee as the facility.
Regarding Claim(s) 3, Takayama teaches: The household job point calculation method according to claim 2, further comprising setting a third party coefficient for a user who uses an appliance or a facility manufactured by a specific manufacturer to be larger than a third party coefficient for a user who uses an appliance or a facility manufactured by another manufacturer. (Takayama, [91-92]; the labor fee calculation unit acquires the location identification number “100” of the location information received by the location information receiving unit. FIG. 16 shows data of the cleaning place, the labor fee, and the location information of the search key. In FIG. 16, the labor rate calculation unit uses the location identification number “100” of the acquired location information as a search key and finds out the corresponding data from the cleaning location and labor rate data previously recorded in a memory built in the information processing apparatus. Obtain a labor charge of "3,000" for the cleaning place… The labor fee calculation unit acquires the location identification number “200” of the location information received by the location information receiving unit next. The labor fee calculation unit acquires the labor fee “1,000” of the corresponding cleaning location from the data of the cleaning location and the labor fee using the location identification number “200” as a search key… IG. 17 shows an example of the cleaning target place information. In FIG. 17, the cleaning target location information is, for example, that the cleaning target location identified by the location identification number “100” is “living room” and the cleaning target location identified by the location identification number “200” is “ "Dining"). Examiner interprets the different areas as different facilities, for example a dining room might not require the same manufacturer as a bathroom of kitchen which requires plumbing facilities.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takayama et al. (JP 2004272650 A) in view of Marti et al. (US 20160132030 A1), and Murakami et al. (US 20180129192 A1).
Regarding Claim(s) 4, While Takayama/Marti teaches determining a household job point and a third party coefficient, Takayama does not appear to explicitly teach coefficients associated with a number of appliances or facilities. However, Takayama in view of the analogous art of Murakami (i.e. task management) does teach: The household job point calculation method according to claim 2, further comprising setting a third party coefficient for a user who uses a first number of appliances or facilities manufactured by the specific manufacturer to be larger than a third party coefficient for a user who uses a second number of appliances or facilities manufactured by the specific manufacturer, the second number being smaller than the first number. (Murakami, [47]; More specifically, the job DB 2 has “job ID”, “precedence constraint number pair”, “job time”, “skill level”, “tool/equipment”, “cost of developing robotization”, and “operator number before reconfiguration” fields as illustrated in FIG. 4A. The “job ID” field stores an identification number of the job (a two-digit number subsequent to “Job” illustrated in FIG. 7 through FIG. 8B). The “precedence constraint number pair” field stores a number of pair representing the parent-child relation of the job. The precedence constraint number pair enables to know which work is to be done next to which work. The details of the precedence constraint number pair will be described later. The “job time” field stores the amount of time (second) it takes to complete the job. The “skill level” field stores the skill level permitted to execute the job. For example, a job with a skill level of 2 is a job that an operator with a skill level of 2 or greater (2, 3 . . . ) is permitted to execute and Murakami, [58]; The labor cost DB 4 has a data structure illustrated in FIG. 5A. The labor cost DB 4 illustrated in FIG. 5A is a database that stores information about the base salaries of workers on a skill level basis. The labor cost DB may be a labor cost DB 4′ illustrated in FIG. 5B that stores information about the base salary and the skill level of each worker). Examiner interprets the different areas as different facilities, for example a dining room might not require the same manufacturer as a bathroom of kitchen which requires plumbing facilities and Murakami, [Fig. 4A and Fig. 5A]; Examiner notes that the skill levels are associated with more appliances/facilities are associated with higher monthly salaries (i.e. coefficients) as shown in Fig. 5A).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Takayama including determining a household job point and a third party coefficient, with the teachings of Murakami including coefficients associated with a number of appliances or facilities in order to ensure the users are qualified for specific task (Murakami, [47]; The “skill level” field stores the skill level permitted to execute the job. For example, a job with a skill level of 2 is a job that an operator with a skill level of 2 or greater (2, 3 . . . ) is permitted to execute).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takayama et al. (JP 2004272650 A) in view of Marti et al. (US 20160132030 A1), and Neumann et al. (US 12073296 B2).
Regarding Claim(s) 6, Takayama/Marti teaches calculating the household job point on the basis of the household job data, the third party coefficient information, and …. information. (Takayama, [51]; For example, when the day of the week is Sunday, the labor rate is a value calculated by multiplying the labor rate calculated from the operation information by 1.3. This 1.3 is an arbitrary value such as multiplying by 1.0 when the day of the week is a weekday and Takayama, [98]; The labor fee calculation unit calculates the labor fee based on the location identification number of the location information and the reception time corresponding thereto. The labor fee calculation unit reads the reception time, and performs a process of multiplying the labor fee by a correction coefficient when the reception time is within a certain time range from the next reception time. This is because the cleaner can clean the same place more than necessary and can judge that proper cleaning has not been performed. For example, the labor fee calculation unit calculates the labor fee “2,400” by multiplying “3,000” acquired using the location identification number as a search key by a correction coefficient “0.8”). Examiner notes that Takayama teaches determining a household job point by multiplying job data, by a third party coefficient and further multiplies by an additional coefficients although not explicitly stating a user coefficient. Examiner notes that Neumann below explicitly recites a user coefficient.
While Takayama teaches the use of coefficients to determine household job points, Takayama does not appear to explicitly teach the use of a user coefficient. However, the prior cited art in view of the analogous art of Neumann (i.e. activity tracking) does teach: The household job point calculation method according to claim 1, further comprising: acquiring user coefficient information indicating a user coefficient for a user; and (Neumann, [col. 9, lines 54-10]; With continued reference to FIG. 1, an activity profile 112 includes at least an element of human subject data. “Human subject data” as used in this disclosure, is data describing a physical activity the subject may have been engaged in either before, during, or after collection of a signal. “Activity” as used in this disclosure, includes any action that requires physical effort which may be performed to maintain, sustain, and/or improve one's health. Activity may include movement that a user may engage upon such as walking around subject's house, shoveling snow on subject's driveway, performing movement at a gym such as by exercising on a machine such as a treadmill or Stairmaster, participating in a group exercise class, performing a meditation sequence, practicing a yoga sequence, lifting weights, performing one or more exercise routines, taking a leisurely stroll, performing a series of stretches, and the like. Activity may include subconscious movement such as one's body movements while sleeping that include rolling from side to side while sleeping. Activity may include one or more exertions such as folding laundry, performing housework, cleaning dishes, dusting, carrying groceries, and the like. User activity data may include a description of one or more activities that a user engages in either before, during, or after collection of a signal).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Takayama including the use of coefficients to determine household job points with the teachings of Neumann including the use of user coefficients in order to better determine what a user will be able to perform (Neumann, [col. 12, lines 35-45]; An activity profile 112 may include a description of one or more physical activities that a human subject classified to an activity profile 112 is expected to be able to perform. An activity profile 112 may include a description of one or more target ranges, reference ranges, usual responses, and/or findings that a biological extraction should fall within for a subject with a certain activity profile 112. An activity profile 112 may include a selection of one or more training sets that may be relevant for a subject who may be classified to an activity profile 112 and Neumann, [col. 29, lines 19-22]; Classifier 136 may output a fitness profile by evaluating a user's age, sex, demographic information, and/or any other information that may be relevant pertaining to what exercises and/or routines that a user may or may not be able to perform).
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takayama et al. (JP 2004272650 A) in view of Marti et al. (US 20160132030 A1), and Salehian et al. (US 20200100704 A1).
Regarding Claim(s) 7, Takayama teaches: calculating the household job point on the basis of the household job data, the third party coefficient information, and the attribute coefficient information. (Takayama, [51]; For example, when the day of the week is Sunday, the labor rate is a value calculated by multiplying the labor rate calculated from the operation information by 1.3. This 1.3 is an arbitrary value such as multiplying by 1.0 when the day of the week is a weekday and Takayama, [98]; The labor fee calculation unit calculates the labor fee based on the location identification number of the location information and the reception time corresponding thereto. The labor fee calculation unit reads the reception time, and performs a process of multiplying the labor fee by a correction coefficient when the reception time is within a certain time range from the next reception time. This is because the cleaner can clean the same place more than necessary and can judge that proper cleaning has not been performed. For example, the labor fee calculation unit calculates the labor fee “2,400” by multiplying “3,000” acquired using the location identification number as a search key by a correction coefficient “0.8”). Examiner notes that Takayama teaches determining a household job point by multiplying job data, by a third party coefficient and further multiplies by an additional coefficients although not explicitly stating a user coefficient. Examiner notes that Salehian below explicitly teaches an attribute coefficient.
While Takayama teaches the use of coefficients to determine household job points, neither appear to explicitly teach the use of an attribute coefficient. However, the prior cited art in view of the analogous art of Salehian (i.e. activity tracking) does teach: The household job point calculation method according to claim 1, further comprising: acquiring attribute coefficient information indicating an attribute coefficient in connection with an attribute of a user; and (Salehian, [04-05]; the at least one physiological characteristic of the respective user; determining a regression constant for the at least one gait metric model by performing a regression of the second historical run data; receiving first run data from an activity monitoring device carried by a first user during a first run of the first user; determining the gait metric for the first run based on the first run data; determining a pace during the first run based on the first run data; determining a gait metric target for the first run based on the at least one gait metric model, the determined regression coefficients, the determined regression constant, the pace during the first run, and the at least one physiological characteristic of the first user; and displaying a comparison of the gait metric with the gait metric target to the first user on a personal electronic device associated with the first user…. fitness tracking system comprises a database configured to store: first historical run data regarding runs of a first plurality of users, the first historical run data including, for each run, a gait metric for the respective run, a pace during the respective run, and at least one physiological characteristic of the respective user, the gait metric being at least one of (i) a stride cadence and (ii) a stride length; and second historical run data regarding runs of a second plurality of users, the second historical run data including, for each run, the gait metric for the respective run, a pace during the respective run, and the at least one physiological characteristic of the respective user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Takayama including the use of coefficients to determine household job points with the teachings of Salehian including the use of attribute coefficients in order to provide better coaching for users when performing tasks as needed (Salehian, [133]; the ‘shape’ of the gait metric model is determined based on running data from a broad diverse population of users, thereby providing a robust estimation of how pace and physiological characteristics such height, age, weight, and sex influence the value for the at least one gait metric. At the same time, the offset and/or Y-axis intercept of the gait metric model is determined based on expert running data from a more limited set of expert users, thereby providing a better indication of what an optimal and/or efficient value for the at least one gait metric. Devices that are able to use the gait metric model developed in this way can operate more efficiently to provide useful and effective gait coaching to users).
Regarding Claim(s) 8, While Takayama/Marti teaches determining a household job point and a third party coefficient, Takayama does not appear to explicitly teach acquiring user specific change to change a coefficient. However, Takayama in view of the analogous art of Salehian does teach: The household job point calculation method according to claim 1, further comprising: acquiring, from a specific user, change information indicating a change in the third party coefficient; and (Salehian, [04-05]; the at least one physiological characteristic of the respective user; determining a regression constant for the at least one gait metric model by performing a regression of the second historical run data; receiving first run data from an activity monitoring device carried by a first user during a first run of the first user; determining the gait metric for the first run based on the first run data; determining a pace during the first run based on the first run data; determining a gait metric target for the first run based on the at least one gait metric model, the determined regression coefficients, the determined regression constant, the pace during the first run, and the at least one physiological characteristic of the first user; and displaying a comparison of the gait metric with the gait metric target to the first user on a personal electronic device associated with the first user…. he fitness tracking system comprises a database configured to store: first historical run data regarding runs of a first plurality of users, the first historical run data including, for each run, a gait metric for the respective run, a pace during the respective run, and at least one physiological characteristic of the respective user, the gait metric being at least one of (i) a stride cadence and (ii) a stride length; and second historical run data regarding runs of a second plurality of users, the second historical run data including, for each run, the gait metric for the respective run, a pace during the respective run, and the at least one physiological characteristic of the respective user).
changing the third party coefficient on the basis of the change information. (Salehian, [04-06]; receiving second historical run data regarding runs of a second plurality of users, the second historical run data including, for each run, the gait metric for the respective run, a pace during the respective run, and the at least one physiological characteristic of the respective user; determining a regression constant for the at least one gait metric model by performing a regression of the second historical run data; receiving first real-time run data from an activity monitoring device carried by a first user during a first run of the first user; determining a real-time value of the gait metric during the first run based on the first real-time run data; determining a real-time pace during the first run based on the first real-time run data; determining a real-time gait metric target during the first run based on the at least one gait metric model, the determined regression coefficients, the determined regression constant, the real-time pace during the first run, and the at least one physiological characteristic of the first user; and providing perceptible feedback to the first user during the first run depending on a comparison of the real-time value of the gait metric with the real-time gait metric target to the first user using a personal electronic device associated with the first user). Examiner notes that Salehian discloses changing coefficients over time taking into account user activity.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Takayama including the use of coefficients to determine household job points with the teachings of Salehian including the use of changing coefficients in order to provide better coaching for users when performing tasks as needed (Salehian, [133]; the ‘shape’ of the gait metric model is determined based on running data from a broad diverse population of users, thereby providing a robust estimation of how pace and physiological characteristics such height, age, weight, and sex influence the value for the at least one gait metric. At the same time, the offset and/or Y-axis intercept of the gait metric model is determined based on expert running data from a more limited set of expert users, thereby providing a better indication of what an optimal and/or efficient value for the at least one gait metric. Devices that are able to use the gait metric model developed in this way can operate more efficiently to provide useful and effective gait coaching to users).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takayama et al. (JP 2004272650 A) in view of Marti et al. (US 20160132030 A1), Neumann et al. (US 12073296 B2), and Salehian et al. (US 20200100704 A1).
Regarding Claim(s) 9, While Takayama/Marti teaches household point calculation with a plurality of user (Takayama, [59, 61, etc.]; discusses cleaner identification numbers associated with different users), Takayama does not appear to teach: The household job point calculation method according to claim 8, further comprising setting the specific user on the basis of household job data of a plurality of users in a predetermined period. However, Takayama/Neumann does teach the entirety of the limitation: (Neumann, [col. 10, lines 1-64]; With continued reference to FIG. 1, computing device 104 is configured to generate using the activity profile 112 a physical activity. “Physical activity” as used in this disclosure, is a suggested exercise, fitness action, and/or effort a subject may perform. Physical activities 144 may include exercise activities such as hiking, biking, swimming, weightlifting, meditation, yoga, Pilates, martial arts, or any other physical activity. Physical activity 144 may include different groups of exercises such as cardiovascular activities, strength and toning activities, meditative activities, relaxing activities, and the like. Physical activity 144 may include specific implementation details that contain information describing duration, frequency, periodicity concerning when a subject is advised to practice a particular exercise, including magnitude and intensity regarding the exertion a subject should practice a particular exercise at, and the like. For instance in non-limiting illustrative example, physical activity 144 may include a suggestion that includes a low-impact cardiovascular exercises such as swimming 1500 meters two days each week and practicing yoga for one-hour periods, three days each week at a moderate intensity). Neumann teaches a specific user performing an activity at a specific intensity including household chores.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Takayama including the use of coefficients to determine household job points with the teachings of Neumann including the use of specific users at predetermined time period in order to ensure the user is capable of performing the task (Neumann, [col. 12, lines 35-45]; An activity profile 112 may include a description of one or more physical activities that a human subject classified to an activity profile 112 is expected to be able to perform. An activity profile 112 may include a description of one or more target ranges, reference ranges, usual responses, and/or findings that a biological extraction should fall within for a subject with a certain activity profile 112. An activity profile 112 may include a selection of one or more training sets that may be relevant for a subject who may be classified to an activity profile 112 and Neumann, [col. 29, lines 19-22]; Classifier 136 may output a fitness profile by evaluating a user's age, sex, demographic information, and/or any other information that may be relevant pertaining to what exercises and/or routines that a user may or may not be able to perform).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingl